metadat/0000755000176200001440000000000014750517112011674 5ustar liggesusersmetadat/tests/0000755000176200001440000000000014223077101013030 5ustar liggesusersmetadat/tests/testthat/0000755000176200001440000000000014750517112014676 5ustar liggesusersmetadat/tests/testthat/test_dat.bourassa1996.r0000644000176200001440000000031314167070054021035 0ustar liggesuserscontext("Checking: dat.bourassa1996") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bourassa1996, algo="md5"), "ee1fd46dc621922f30596af07ab12e31") }) metadat/tests/testthat/test_dat.egger2001.r0000644000176200001440000000030514167070054020262 0ustar liggesuserscontext("Checking: dat.egger2001") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.egger2001, algo="md5"), "390720b458d2f33ffa043578d6b30654") }) metadat/tests/testthat/test_dat.raudenbush1985.r0000644000176200001440000000031714167070054021360 0ustar liggesuserscontext("Checking: dat.raudenbush1985") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.raudenbush1985, algo="md5"), "61c451c67c7fc82375305c017c6f524a") }) metadat/tests/testthat/test_dat.li2007.r0000644000176200001440000000027714167070054017613 0ustar liggesuserscontext("Checking: dat.li2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.li2007, algo="md5"), "48172cc79e9a18e841fa23cb35d5b756") }) metadat/tests/testthat/test_dat.berkey1998.r0000644000176200001440000000030714167070054020504 0ustar liggesuserscontext("Checking: dat.berkey1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.berkey1998, algo="md5"), "c94bd13f07a693a3c81b44903afad980") }) metadat/tests/testthat/test_dat.vanhowe1999.r0000644000176200001440000000031114167070054020666 0ustar liggesuserscontext("Checking: dat.vanhowe1999") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.vanhowe1999, algo="md5"), "3300d41b67fb79766dbb1e8fff7e50b0") }) metadat/tests/testthat/test_dat.bonett2010.r0000644000176200001440000000030714167070054020466 0ustar liggesuserscontext("Checking: dat.bonett2010") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bonett2010, algo="md5"), "ddf3b996a39cc24f594d6c2419275c99") }) metadat/tests/testthat/test_dat.hasselblad1998.r0000644000176200001440000000031714167070054021326 0ustar liggesuserscontext("Checking: dat.hasselblad1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hasselblad1998, algo="md5"), "40a55e4cef50d2bd0430de5163d19eb0") }) metadat/tests/testthat/test_dat.baker2009.r0000644000176200001440000000030514167070054020265 0ustar liggesuserscontext("Checking: dat.baker2009") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.baker2009, algo="md5"), "80fe19afb3dca58ca7be09b65cb2de4f") }) metadat/tests/testthat/test_dat.damico2009.r0000644000176200001440000000030714167070054020437 0ustar liggesuserscontext("Checking: dat.damico2009") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.damico2009, algo="md5"), "4b1d4adde9227dee3b31b0e109adb938") }) metadat/tests/testthat/test_dat.ursino2021.r0000644000176200001440000000030714344132547020517 0ustar liggesuserscontext("Checking: dat.ursino2021") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.ursino2021, algo="md5"), "b9c5821c858a7d8a5501822a3873c998") }) metadat/tests/testthat/test_dat.linde2016.r0000644000176200001440000000030514167107602020272 0ustar liggesuserscontext("Checking: dat.linde2016") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.linde2016, algo="md5"), "33020cfa2aed4478bac3364b0dd3a9dd") }) metadat/tests/testthat/test_dat.spooner2002.r0000644000176200001440000000031114657354430020663 0ustar liggesuserscontext("Checking: dat.spooner2002") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.spooner2002, algo="md5"), "d35bdc852b7c6204236240f217046470") }) metadat/tests/testthat/test_dat.pignon2000.r0000644000176200001440000000030714167070054020464 0ustar liggesuserscontext("Checking: dat.pignon2000") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.pignon2000, algo="md5"), "50b607c84d1ac4cdf03ce4cbcc7a7e20") }) metadat/tests/testthat/test_dat.senn2013.r0000644000176200001440000000030314167070054020135 0ustar liggesuserscontext("Checking: dat.senn2013") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.senn2013, algo="md5"), "b845a2bc3436efff179e8c34cb99602a") }) metadat/tests/testthat/test_dat.curtis1998.r0000644000176200001440000000030714167070054020534 0ustar liggesuserscontext("Checking: dat.curtis1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.curtis1998, algo="md5"), "a6007ef1e665877559ca0d0e0315aa4c") }) metadat/tests/testthat/test_dat.collins1985a.r0000644000176200001440000000031314167070054021020 0ustar liggesuserscontext("Checking: dat.collins1985a") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.collins1985a, algo="md5"), "273f562fb6389816a296afd7fa89ab9c") }) metadat/tests/testthat/test_dat.hartmannboyce2018.r0000644000176200001440000000032514223077651022040 0ustar liggesuserscontext("Checking: dat.hartmannboyce2018") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hartmannboyce2018, algo="md5"), "5f8bbde96e75d8e8da450575a6b72f86") }) metadat/tests/testthat/test_dat.linde2015.r0000644000176200001440000000030514167107540020272 0ustar liggesuserscontext("Checking: dat.linde2015") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.linde2015, algo="md5"), "003effb9075e234e4c1b7e641c85ace9") }) metadat/tests/testthat/test_dat.cannon2006.r0000644000176200001440000000030714167070054020454 0ustar liggesuserscontext("Checking: dat.cannon2006") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.cannon2006, algo="md5"), "63a03f32e5f26afa8e0290b74d47655d") }) metadat/tests/testthat/test_dat.molloy2014.r0000644000176200001440000000030714167070054020512 0ustar liggesuserscontext("Checking: dat.molloy2014") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.molloy2014, algo="md5"), "289c001827c4d4104b2c02bd71334469") }) metadat/tests/testthat/test_dat.mcdaniel1994.r0000644000176200001440000000031314167070054020770 0ustar liggesuserscontext("Checking: dat.mcdaniel1994") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.mcdaniel1994, algo="md5"), "6d9c49bc2437cd8f5cc63c38d0bc917d") }) metadat/tests/testthat/test_dat.yusuf1985.r0000644000176200001440000000030514167070054020370 0ustar liggesuserscontext("Checking: dat.yusuf1985") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.yusuf1985, algo="md5"), "1204e1dd2a90ae553308cfcbe0dfb5fd") }) metadat/tests/testthat/test_dat.gibson2002.r0000644000176200001440000000030714167070054020455 0ustar liggesuserscontext("Checking: dat.gibson2002") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.gibson2002, algo="md5"), "5629cee5d47e75d3add44dc91ac5e4ac") }) metadat/tests/testthat/test_dat.dumouchel1994.r0000644000176200001440000000031514417201110021166 0ustar liggesuserscontext("Checking: dat.dumouchel1994") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.dumouchel1994, algo="md5"), "da9f347c3df75b8fe0c75f8e3030ad16") }) metadat/tests/testthat/test_dat.franchini2012.r0000644000176200001440000000031514167107436021142 0ustar liggesuserscontext("Checking: dat.franchini2012") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.franchini2012, algo="md5"), "032fe3c5d34b4479f354fffc0dafe72f") }) metadat/tests/testthat/test_dat.curtin2002.r0000644000176200001440000000030714657354353020513 0ustar liggesuserscontext("Checking: dat.curtin2002") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.curtin2002, algo="md5"), "3f6600770272aa68c2720dbfb78d0966") }) metadat/tests/testthat/test_dat.moura2021.r0000644000176200001440000000053714215673340020326 0ustar liggesuserscontext("Checking: dat.moura2021") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.moura2021$dat, algo="md5"), "313e588a5e1d08ee541260392beb9ae3") }) test_that("md5 hash matches for the tree", { expect_match(digest(metadat::dat.moura2021$tree, algo="md5"), "78b3738727e054b2212cd16ebf1d5c6a") }) metadat/tests/testthat/test_dat.pritz1997.r0000644000176200001440000000030514167070054020370 0ustar liggesuserscontext("Checking: dat.pritz1997") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.pritz1997, algo="md5"), "d404c9d72e033e67105c0f1bee3a20fc") }) metadat/tests/testthat/test_dat.knapp2017.r0000644000176200001440000000030514167070054020311 0ustar liggesuserscontext("Checking: dat.knapp2017") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.knapp2017, algo="md5"), "0d866a1328248f8c6458c5127c443e02") }) metadat/tests/testthat/test_dat.lee2004.r0000644000176200001440000000030114167070054017735 0ustar liggesuserscontext("Checking: dat.lee2004") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.lee2004, algo="md5"), "3d79eee16145524fe53554e9c43109e4") }) metadat/tests/testthat/test_dat.frank2008.r0000644000176200001440000000030514216157151020300 0ustar liggesuserscontext("Checking: dat.frank2008") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.frank2008, algo="md5"), "e0ac95d1768ec2fbd61dd7907434cb7e") }) metadat/tests/testthat/test_dat.begg1989.r0000644000176200001440000000030314167070054020123 0ustar liggesuserscontext("Checking: dat.begg1989") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.begg1989, algo="md5"), "56f50ca9aa6581fd843bd1083725ccea") }) metadat/tests/testthat/test_dat.craft2003.r0000644000176200001440000000030514167070054020272 0ustar liggesuserscontext("Checking: dat.craft2003") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.craft2003, algo="md5"), "0e314716e890ef8c3366d01c447e8992") }) metadat/tests/testthat/test_dat.roever2022.r0000644000176200001440000000030714344132513020474 0ustar liggesuserscontext("Checking: dat.roever2022") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.roever2022, algo="md5"), "1409e1999fa055ff3728478033bd5da2") }) metadat/tests/testthat/test_dat.pagliaro1992.r0000644000176200001440000000031314167070054021010 0ustar liggesuserscontext("Checking: dat.pagliaro1992") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.pagliaro1992, algo="md5"), "7560844add63d1c6c7b44847b05da7aa") }) metadat/tests/testthat/test_dat.axfors2021.r0000644000176200001440000000030714167070054020477 0ustar liggesuserscontext("Checking: dat.axfors2021") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.axfors2021, algo="md5"), "d7f19500e14f2b8a383cefac7791f43f") }) metadat/tests/testthat/test_dat.maire2019.r0000644000176200001440000000055214167070054020303 0ustar liggesuserscontext("Checking: dat.maire2019") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.maire2019$dat, algo="md5"), "a1bea482e3f89fad844e08b7b3d3d987") }) test_that("md5 hash matches for the distance matrix", { expect_match(digest(metadat::dat.maire2019$dmat, algo="md5"), "bc75d00397a71dc2c7a7e1c5f88f7e79") }) metadat/tests/testthat/test_dat.dogliotti2014.r0000644000176200001440000000031514167107353021177 0ustar liggesuserscontext("Checking: dat.dogliotti2014") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.dogliotti2014, algo="md5"), "8dde5871843a3ed7c62c54ae84474234") }) metadat/tests/testthat/test_dat.white2020.r0000644000176200001440000000030514167070054020312 0ustar liggesuserscontext("Checking: dat.white2020") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.white2020, algo="md5"), "290a7ba0476ec60c726604ec04c8d437") }) metadat/tests/testthat/test_dat.graves2010.r0000644000176200001440000000030714167070054020462 0ustar liggesuserscontext("Checking: dat.graves2010") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.graves2010, algo="md5"), "3bff074ad3ea13da4c5e67f33265709d") }) metadat/tests/testthat/test_dat.bornmann2007.r0000644000176200001440000000031314167070054021010 0ustar liggesuserscontext("Checking: dat.bornmann2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bornmann2007, algo="md5"), "cd3be7c49eed994719c0f4b224e48f24") }) metadat/tests/testthat/test_dat.dorn2007.r0000644000176200001440000000030314167070054020137 0ustar liggesuserscontext("Checking: dat.dorn2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.dorn2007, algo="md5"), "1cd82454da033a25ba5d831b1626dff5") }) metadat/tests/testthat/test_dat.bangertdrowns2004.r0000644000176200001440000000032514167070054022055 0ustar liggesuserscontext("Checking: dat.bangertdrowns2004") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bangertdrowns2004, algo="md5"), "716b3c863ecc3fd61d6a9c076732f28c") }) metadat/tests/testthat/test_dat.laopaiboon2015.r0000644000176200001440000000031714167070054021324 0ustar liggesuserscontext("Checking: dat.laopaiboon2015") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.laopaiboon2015, algo="md5"), "1260edb7d5c69d8bc0b507f16eaacd0c") }) metadat/tests/testthat/test_dat.riley2003.r0000644000176200001440000000030514167070054020317 0ustar liggesuserscontext("Checking: dat.riley2003") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.riley2003, algo="md5"), "606d5e3307678b0b8b57b0a833f4d184") }) metadat/tests/testthat/test_dat.woods2010.r0000644000176200001440000000030514167107655020334 0ustar liggesuserscontext("Checking: dat.woods2010") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.woods2010, algo="md5"), "7159dcbf42d9bc82d53959b6fd2d8b9a") }) metadat/tests/testthat/test_dat.besson2016.r0000644000176200001440000000030714167070054020472 0ustar liggesuserscontext("Checking: dat.besson2016") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.besson2016, algo="md5"), "37e9119b7f262598c930291ae0fac9af") }) metadat/tests/testthat/test_dat.lau1992.r0000644000176200001440000000030114167070054017770 0ustar liggesuserscontext("Checking: dat.lau1992") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.lau1992, algo="md5"), "724e40152d092b1ba8793b20862fbb6a") }) metadat/tests/testthat/test_dat.ishak2007.r0000644000176200001440000000030514167070054020276 0ustar liggesuserscontext("Checking: dat.ishak2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.ishak2007, algo="md5"), "59501e470f16259537abc9ed864cd6f1") }) metadat/tests/testthat/test_dat.cohen1981.r0000644000176200001440000000030514167070054020305 0ustar liggesuserscontext("Checking: dat.cohen1981") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.cohen1981, algo="md5"), "c74ba5b65fb0347e4842ec32276d887a") }) metadat/tests/testthat/test_dat.kearon1998.r0000644000176200001440000000030714167070054020502 0ustar liggesuserscontext("Checking: dat.kearon1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.kearon1998, algo="md5"), "46bb5ea53ba8d6bb22d3cceb2f8429ac") }) metadat/tests/testthat/test_dat.nielweise2007.r0000644000176200001440000000031514167070054021164 0ustar liggesuserscontext("Checking: dat.nielweise2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.nielweise2007, algo="md5"), "05e2f281b42f16fb6be456f62b625f82") }) metadat/tests/testthat/test_dat.aloe2013.r0000644000176200001440000000030314167070054020112 0ustar liggesuserscontext("Checking: dat.aloe2013") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.aloe2013, algo="md5"), "bbb5d1a536e791eba68780bb8822125a") }) metadat/tests/testthat/test_dat.collins1985b.r0000644000176200001440000000031314167070054021021 0ustar liggesuserscontext("Checking: dat.collins1985b") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.collins1985b, algo="md5"), "a1c5ab9525f7ad9354793073bb106164") }) metadat/tests/testthat/test_dat.obrien2003.r0000644000176200001440000000030714167070054020453 0ustar liggesuserscontext("Checking: dat.obrien2003") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.obrien2003, algo="md5"), "f46831024c5e7c0ca64607327fa1987e") }) metadat/tests/testthat/test_dat.viechtbauer2021.r0000644000176200001440000000032114167070054021472 0ustar liggesuserscontext("Checking: dat.viechtbauer2021") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.viechtbauer2021, algo="md5"), "2dd6203150534b3bd62dba7a329ffdf4") }) metadat/tests/testthat/test_dat.assink2016.r0000644000176200001440000000030714467432013020471 0ustar liggesuserscontext("Checking: dat.assink2016") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.assink2016, algo="md5"), "4b542f0f3e66323a547b58c57d488568") }) metadat/tests/testthat/test_dat.bassler2004.r0000644000176200001440000000031114657354317020637 0ustar liggesuserscontext("Checking: dat.bassler2004") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bassler2004, algo="md5"), "b2602c6ffa358e30992b5de028bcc1ff") }) metadat/tests/testthat/test_dat.lim2014.r0000644000176200001440000000256614167070054017771 0ustar liggesuserscontext("Checking: dat.lim2014") library(digest) test_that("md5 hash matches for the data m_o_size", { expect_match(digest(metadat::dat.lim2014$m_o_size, algo="md5"), "47c9b90b4d399147238b3d001e8cc09b") }) test_that("md5 hash matches for the data m_o_fecundity", { expect_match(digest(metadat::dat.lim2014$m_o_fecundity, algo="md5"), "5cd73c511ca7a5993d74ea3a0b01e7a4") }) test_that("md5 hash matches for the data o_o_unadj", { expect_match(digest(metadat::dat.lim2014$o_o_unadj, algo="md5"), "281630552862a8351684c375ca2f237c") }) test_that("md5 hash matches for the data o_o_adj", { expect_match(digest(metadat::dat.lim2014$o_o_adj, algo="md5"), "86870c289d85e1d914ae009bf9635529") }) test_that("md5 hash matches for the tree m_o_size_tree", { expect_match(digest(metadat::dat.lim2014$m_o_size_tree, algo="md5"), "a0d9c3c1381669abb23710059752cd18") }) test_that("md5 hash matches for the tree m_o_fecundity_tree", { expect_match(digest(metadat::dat.lim2014$m_o_fecundity_tree, algo="md5"), "553fb55660b195c5f319c147585e816e") }) test_that("md5 hash matches for the tree o_o_unadj_tree", { expect_match(digest(metadat::dat.lim2014$o_o_unadj_tree, algo="md5"), "d4006f261ce75e7cb397b1fdefe7f7c6") }) test_that("md5 hash matches for the tree o_o_adj_tree", { expect_match(digest(metadat::dat.lim2014$o_o_adj_tree, algo="md5"), "ea8b3394bef1ea32001e903b987ded89") }) metadat/tests/testthat/test_dat.crede2010.r0000644000176200001440000000030514167070054020253 0ustar liggesuserscontext("Checking: dat.crede2010") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.crede2010, algo="md5"), "e41ba72533831f037300d67c501a09eb") }) metadat/tests/testthat/test_dat.dong2013.r0000644000176200001440000000030314167107403020120 0ustar liggesuserscontext("Checking: dat.dong2013") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.dong2013, algo="md5"), "e94d834a5af363dfb336bf642f790957") }) metadat/tests/testthat/test_dat.nielweise2008.r0000644000176200001440000000031514167070054021165 0ustar liggesuserscontext("Checking: dat.nielweise2008") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.nielweise2008, algo="md5"), "f7e4d793597dadf2d314545af7555d73") }) metadat/tests/testthat/test_dat.bartos2023.r0000644000176200001440000000030714521174302020464 0ustar liggesuserscontext("Checking: dat.bartos2023") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bartos2023, algo="md5"), "fe61d69f402cb3871713264daa903621") }) metadat/tests/testthat/test_dat.stowe2010.r0000644000176200001440000000030514167107631020334 0ustar liggesuserscontext("Checking: dat.stowe2010") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.stowe2010, algo="md5"), "2191ce23321ec455c23daf088e09bea0") }) metadat/tests/testthat/test_dat.hahn2001.r0000644000176200001440000000030314167070054020105 0ustar liggesuserscontext("Checking: dat.hahn2001") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hahn2001, algo="md5"), "53da7f29fb9d9d3c73af596862c5a9a9") }) metadat/tests/testthat/test_dat.fine1993.r0000644000176200001440000000030314167070054020133 0ustar liggesuserscontext("Checking: dat.fine1993") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.fine1993, algo="md5"), "34a037b6745cf6b6e9afd6bb2aed7007") }) metadat/tests/testthat/test_dat.normand1999.r0000644000176200001440000000031114167070054020655 0ustar liggesuserscontext("Checking: dat.normand1999") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.normand1999, algo="md5"), "35ddafb737cfa2d73816e9d4c5c8dd76") }) metadat/tests/testthat/test_dat.hannum2020.r0000644000176200001440000000030714167070054020462 0ustar liggesuserscontext("Checking: dat.hannum2020") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hannum2020, algo="md5"), "e935f7ffa13d303a2b955700f07600ad") }) metadat/tests/testthat/test_dat.baskerville2012.r0000644000176200001440000000032114167070054021474 0ustar liggesuserscontext("Checking: dat.baskerville2012") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.baskerville2012, algo="md5"), "fe6765cbaa93d7096aced942cdb5b32e") }) metadat/tests/testthat/test_dat.lopez2019.r0000644000176200001440000000030514167070054020333 0ustar liggesuserscontext("Checking: dat.lopez2019") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.lopez2019, algo="md5"), "7c4e427c79baa8549fb3c46a1aff7265") }) metadat/tests/testthat/test_dat.nakagawa2007.r0000644000176200001440000000031314167070054020750 0ustar liggesuserscontext("Checking: dat.nakagawa2007") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.nakagawa2007, algo="md5"), "aef00ca12dac2cd7c27d7a8f7692ba62") }) metadat/tests/testthat/test_dat.kalaian1996.r0000644000176200001440000000031114411522670020612 0ustar liggesuserscontext("Checking: dat.kalaian1996") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.kalaian1996, algo="md5"), "0f15c3094fef881f444ceeeaf54771a4") }) metadat/tests/testthat/test_dat.colditz1994.r0000644000176200001440000000031114167070054020662 0ustar liggesuserscontext("Checking: dat.colditz1994") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.colditz1994, algo="md5"), "a8df61024006ae1ce670b7ede5b29a9d") }) metadat/tests/testthat/test_dat.crisafulli2020.r0000644000176200001440000000031714417200755021333 0ustar liggesuserscontext("Checking: dat.crisafulli2020") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.crisafulli2020, algo="md5"), "fee9264067c19c6e53294e4c72eec7ef") }) metadat/tests/testthat/test_dat.landenberger2005.r0000644000176200001440000000032314167070054021625 0ustar liggesuserscontext("Checking: dat.landenberger2005") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.landenberger2005, algo="md5"), "787f5762af4701c33cf1ca7548e54fea") }) metadat/tests/testthat/test_dat.gurusamy2011.r0000644000176200001440000000031314167107476021056 0ustar liggesuserscontext("Checking: dat.gurusamy2011") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.gurusamy2011, algo="md5"), "fafe3c72ffac20364548bca7a0cbe866") }) metadat/tests/testthat/test_dat.bakdash2021.r0000644000176200001440000000031114167070054020565 0ustar liggesuserscontext("Checking: dat.bakdash2021") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bakdash2021, algo="md5"), "cb2b2109505430e49ded3586a3ba31a1") }) metadat/tests/testthat/test_dat.tannersmith2016.r0000644000176200001440000000032114205661725021535 0ustar liggesuserscontext("Checking: dat.tannersmith2016") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.tannersmith2016, algo="md5"), "bda45667a6c6b6e7c24c5abfc91590de") }) metadat/tests/testthat/test_dat.furukawa2003.r0000644000176200001440000000031314657354402021025 0ustar liggesuserscontext("Checking: dat.furukawa2003") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.furukawa2003, algo="md5"), "e9f22be16a3dfd4832663ede1fe9d462") }) metadat/tests/testthat/test_dat.dagostino1998.r0000644000176200001440000000031514167070054021211 0ustar liggesuserscontext("Checking: dat.dagostino1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.dagostino1998, algo="md5"), "fa0804ed25a8d0d93f91d840a315162a") }) metadat/tests/testthat/test_dat.anand1999.r0000644000176200001440000000030514167070054020303 0ustar liggesuserscontext("Checking: dat.anand1999") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.anand1999, algo="md5"), "83ef370cdbe1d5db37f1a3fd98676a80") }) metadat/tests/testthat/test_dat.hine1989.r0000644000176200001440000000030314167070054020142 0ustar liggesuserscontext("Checking: dat.hine1989") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hine1989, algo="md5"), "237ad8966750f5a26cfb917b3365fcfc") }) metadat/tests/testthat/test_dat.konstantopoulos2011.r0000644000176200001440000000033114167070054022453 0ustar liggesuserscontext("Checking: dat.konstantopoulos2011") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.konstantopoulos2011, algo="md5"), "ccc4a7cee29012013552e2dca1c634a4") }) metadat/tests/testthat/test_dat.bcg.r0000644000176200001440000000027114167070054017423 0ustar liggesuserscontext("Checking: dat.bcg") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.bcg, algo="md5"), "a8df61024006ae1ce670b7ede5b29a9d") }) metadat/tests/testthat/test_dat.hackshaw1998.r0000644000176200001440000000031314167070054021011 0ustar liggesuserscontext("Checking: dat.hackshaw1998") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hackshaw1998, algo="md5"), "40bf072f51b31ce8cfb0ca4272eddb84") }) metadat/tests/testthat/test_dat.debruin2009.r0000644000176200001440000000031114167070054020626 0ustar liggesuserscontext("Checking: dat.debruin2009") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.debruin2009, algo="md5"), "d290a731fc4232fbf2bfd54fabcf8732") }) metadat/tests/testthat/test_dat.michael2013.r0000644000176200001440000000031114167070054020573 0ustar liggesuserscontext("Checking: dat.michael2013") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.michael2013, algo="md5"), "ff2a9079b413579d2246e0543c7f0541") }) metadat/tests/testthat/test_dat.hart1999.r0000644000176200001440000000030314167070054020156 0ustar liggesuserscontext("Checking: dat.hart1999") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.hart1999, algo="md5"), "df7f6e390eede443380bb656e9262958") }) metadat/tests/testthat/test_dat.mccurdy2020.r0000644000176200001440000000031114167070054020635 0ustar liggesuserscontext("Checking: dat.mccurdy2020") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.mccurdy2020, algo="md5"), "f3590f669053d881ac7b32fbc378229a") }) metadat/tests/testthat/test_dat.linde2005.r0000644000176200001440000000030514167070054020270 0ustar liggesuserscontext("Checking: dat.linde2005") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.linde2005, algo="md5"), "819177a7eb640f4f89b23ba11f892a5d") }) metadat/tests/testthat/test_dat.lehmann2018.r0000644000176200001440000000031114167070054020620 0ustar liggesuserscontext("Checking: dat.lehmann2018") library(digest) test_that("md5 hash matches for the data", { expect_match(digest(metadat::dat.lehmann2018, algo="md5"), "bfa52ac1cf59ef847c6badf23394298f") }) metadat/tests/testthat.R0000644000176200001440000000023214223075431015014 0ustar liggesusers# to also run skip_on_cran() tests, uncomment: #Sys.setenv(NOT_CRAN="true") library(testthat) library(metadat) test_check("metadat", reporter="summary") metadat/MD50000644000176200001440000004267714750517112012224 0ustar liggesusers49ba2b163504c585b344f8c7bdf66915 *DESCRIPTION faf9a8357bbb8ba7eb8b12b21fbf1702 *NAMESPACE 1de278d86f245e8856f50cc5e8d2fa97 *NEWS.md 1e3f598f7ef36ece242de4d9589b052c *R/datsearch.r 8858bc066441139934e3250cf6a1d8bb *R/prep_dat.r cea7ace0cb3b94b3332627d9071cbd3a *R/rd_generator.r 8c6d806f1c453dcc0e34b5ee7e77e996 *README.md c990cac7da9e5c43e4f83e3f5f580cb7 *build/metadat.pdf b8ddc8a3689c4b8607f100a03b1207d9 *build/stage23.rdb b25cd840961fe0bb9b03bee6caa3e257 *data/dat.aloe2013.rda fc5f94daf3362d2b1e154354f841e9e7 *data/dat.anand1999.rda f94b887a6dc34265d4c66c4481b7eb68 *data/dat.assink2016.rda 5832ff057c37e39e5de3f841eb1c1b94 *data/dat.axfors2021.rda 1d53859fed5869efff8f704afabd0a85 *data/dat.bakdash2021.rda db179c8f4902b7b491eb33d7471069ff *data/dat.baker2009.rda 37889fdbde6df5f8d7ed448b3e2e8e68 *data/dat.bangertdrowns2004.rda 64be8fa9c18ea663f0e579d057a593fc *data/dat.bartos2023.rda d8154ab22478d2c8d902a8a4425ab902 *data/dat.baskerville2012.rda 34f3c60b3b9ba6c56deb0c64c6c64734 *data/dat.bassler2004.rda 3ec26e60ec2de37793d06a9bfa05b73f *data/dat.bcg.rda ce0131936cfb180e7fc71029c21c2b09 *data/dat.begg1989.rda b80c4e0007460a198bcc37132333ae7b *data/dat.berkey1998.rda 7aaf10fdbdd41aa5e2832fcdfa559985 *data/dat.besson2016.rda 3764e051cc3476292ccac59646c654fb *data/dat.bonett2010.rda 5c2e38c73ea1c36d1476fff4154e1be6 *data/dat.bornmann2007.rda ee5c3a590333ba1a58ac96509979fced *data/dat.bourassa1996.rda c3392284eef638200c031a5fbc686463 *data/dat.cannon2006.rda 9ae12121dac07f42f4a5857c925d03a8 *data/dat.cohen1981.rda 314a3003a5cd3ecfd016c2c7f04163ec *data/dat.colditz1994.rda ed7820425a8d716da2f4c6741a010e53 *data/dat.collins1985a.rda 9f9feab52b7227c6e119eb7032e7f337 *data/dat.collins1985b.rda 12c72409e80e51a515190a9b135b9e6e *data/dat.craft2003.rda 89541fe7c8956c973c328eeebe79a607 *data/dat.crede2010.rda 995e7a3d1605c172645ea5e0a170adde *data/dat.crisafulli2020.rda 6cbbfe3767b9250c90a22e3472bc303b *data/dat.curtin2002.rda ac13c3f8cd813d6fb4a0cd2e72ab1b41 *data/dat.curtis1998.rda 57418118aeb7d5cd30d391aec0df4d54 *data/dat.dagostino1998.rda 93eec04820c0831ca7fc1c9c66a236d9 *data/dat.damico2009.rda 6411e291d3e56ad45a19fd17873c8f81 *data/dat.debruin2009.rda 4139df5f20150514bdd078b50d9d0208 *data/dat.dogliotti2014.rda d625b9c740df7d7f503d9bf7ef1ae978 *data/dat.dong2013.rda 6603aae7df999ed48edebe99378ca6c8 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*data/dat.ishak2007.rda 5491eab84713814f603d08dbab67ae7c *data/dat.kalaian1996.rda 081c715da3f46c11ee6f29e4bf79789b *data/dat.kearon1998.rda 020bce9e0557339a605be972f8abdc07 *data/dat.knapp2017.rda 2b4a3edf628b3ca0a0bde4f208d98df8 *data/dat.konstantopoulos2011.rda 22a6fe4e9acb840192446250d20f5a7c *data/dat.landenberger2005.rda 76f3f944cac9a78f9c31bdb420a0ab28 *data/dat.laopaiboon2015.rda 4a13c550bf520fda687c08c9ecdb03a4 *data/dat.lau1992.rda f4d6f921fb1a997ad752a1e7ee790710 *data/dat.lee2004.rda 173b7f273ca800e361653c35ea0448e2 *data/dat.lehmann2018.rda 64f926a97e91ae3455ea29575af4517c *data/dat.li2007.rda 6b9c51bb78d61f10127412f859107005 *data/dat.lim2014.rda f23b6daa1bdf516735d0a4bc1d724fa8 *data/dat.linde2005.rda 7d171d8241fa9faaa87f0e96cbc5853b *data/dat.linde2015.rda 17215921c8b86085d1702281c4024d03 *data/dat.linde2016.rda e72c85c8a68d5a8a3296ae4a4105eb9f *data/dat.lopez2019.rda 670cd887d0869c100fdeea416401c869 *data/dat.maire2019.rda 69652edc29db9dbd840a8da3a660cc10 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overwritten if (!missing(overwrite)) { study_name <- c(study_name, overwrite) study_name <- gsub(".Rd", "", study_name) # remove file ext if need be } # Loop through datasets and create template documentation # Will only be > 1 if overwrite is specified for (i in seq_along(study_name)) { # Open new file connection con <- try(file(file.path(paste0(dir, "/man/"), paste0(study_name[i], ".Rd")), "w")) # Write the single preamble write.table(.preamble_table(study_name[i]), con, row.names = FALSE, col.names = FALSE, quote = FALSE) # Write the meta-data table header write.table(.tabular(study_name[i]), con, row.names = FALSE, col.names = FALSE, quote = FALSE) # Load dataset data <- get(load(paste0(dir, "/data/", as.character(study_name[[i]]), ".rda"))) # Write main metadata write.table(.meta_dat_table(data), con, row.names = FALSE, col.names = FALSE, quote = FALSE, na = "") # Write the postamble write.table(.postamble_table(study_name[i]), con, row.names = FALSE, col.names = FALSE, quote = FALSE) # Close the file connection close(con) } } # Generate preamble .preamble_table <- function(study.name) { name <- paste0("\\name{", study.name, "}") docType <- "\\docType{data}" alias <- paste0("\\alias{", study.name, "}") title <- "\\title{ADD_TITLE}" descrp <- "\\description{ADD_DESCRIPTION}" use <- paste0("\\usage{\n", study.name, "\n}") format <- paste0("\\format{") out <- rbind(name, docType, alias, title, descrp, use, format) return(data.frame(out, stringsAsFactors = FALSE, row.names = 1:nrow(out))) } # Generate table start .tabular <- function(study.name) { info <- paste0("The data frame contains the following columns:") tabular <- "\\tabular{lll}{" out <- rbind(info, tabular) } # Generate metadata table .meta_dat_table <- function(data) { variables <- paste0("\\bold{", colnames(data), "}") type <- paste0("\\tab", " ", "\\code{", as.vector(sapply(data, class)), "}") descrp <- rep(paste0("\\tab", " ADD_DESCRIPTION ", "\\cr"), length = length(variables)) closer <- c("}", NA, NA) meta_dat_table <- cbind(variables, type, descrp, deparse.level = 0) meta_dat_table <- rbind(meta_dat_table, closer) return(data.frame(meta_dat_table, stringsAsFactors = FALSE, row.names = 1:nrow(meta_dat_table))) } # Generate postamble .postamble_table <- function(study.name) { closer <- "}" details <- "\\details{ADD_DETAILS}" source <- "\\source{ADD_REFERENCE}" author <- "\\author{ADD_CONTRIBUTOR_NAME, \\email{ADD_EMAIL}}" eg1 <- "\\examples{" eg2 <- "### copy data into 'dat' and examine data" eg3 <- paste0("dat <- ", study.name) eg4 <- "dat\n" eg5 <- "\\dontrun{\n" eg6 <- "ADD_DETAILED_EXAMPLE\n" keyword <- "\\keyword{datasets}" concept <- "\\concept{ADD_CONCEPT}" out <- rbind(closer, details, source, author, eg1, eg2, eg3, eg4, eg5, eg6, closer, closer, keyword, concept) return(data.frame(out, stringsAsFactors = FALSE, row.names = 1:nrow(out))) } metadat/R/datsearch.r0000644000176200001440000002366114750427322014231 0ustar liggesusersdatsearch <- function(pattern, concept=TRUE, matchall=TRUE, fixed=TRUE, pkgdown=FALSE) { # immediately show warnings when they arise opwarn <- options(warn=1) on.exit(options(warn=opwarn$warn)) # load rdtxt object (list with the plain-text help files) tmpenv <- new.env(parent=emptyenv()) load(paste0(find.package("metadat"), "/help.rdata"), envir=tmpenv) if (missing(pattern)) { interactive <- TRUE cat("\n") } else { interactive <- FALSE } while (TRUE) { if (interactive || is.null(pattern)) pattern <- readline(prompt = "Enter your search term(s) (? for help; to exit): ") if (!is.character(pattern)) { warning("Argument 'pattern' must be a string (vector).") pattern <- NULL next } if (interactive && identical(pattern, "q")) { cat("\n") break } if (interactive && identical(pattern, "?")) { cat("\n") cat("Enter one or multiple search terms at the prompt. Multiple search terms can be\n") cat("separated using a comma, semi-colon, or 'and'. The search either pertains to the\n") cat("concept terms or the full text of the help files. Datasets matching all or any of\n") cat("search terms are returned. For a full-text search, one can specify fixed strings\n") cat("or use regular expressions. Either the standard help file for a chosen dataset will\n") cat("be shown or the corresponding pkgdown docs at https://wviechtb.github.io/metadat/.\n") cat("\n") cat(" key description setting\n") cat(" --------------------------------------------------------------------------------\n") cat(" a list all datasets contained in the metadat package\n") cat(" l list the concept terms that have been used at least once\n") cat(" c toggle between a concept term search or a full-text search ", ifelse(concept, "concept", "full-text"), "\n") cat(" m toggle between matching of all search terms or any of them ", ifelse(matchall, "all", "any"), "\n") cat(" f toggle between fixed string matching or use of regular expressions ", ifelse(fixed, "fixed", "regexp"), "\n") cat(" p toggle between showing the standard help files or the pkgdown docs ", ifelse(pkgdown, "pkgdown", "standard"), "\n") cat("\n") pattern <- NULL next } if (interactive && identical(pattern, "")) { cat("\n") return(invisible()) } if (interactive && identical(pattern, "a")) pattern <- "" if (interactive && identical(pattern, "l")) { cat(" In terms of fields/topics, the following terms have been used at least once: alternative medicine, attraction, cardiology, climate change, covid-19, criminology, dentistry, ecology, education, engineering, epidemiology, evolution, genetics, human factors, medicine, memory, obstetrics, oncology, persuasion, primary care, psychiatry, psychology, smoking, social work, sociology. In terms of outcome measures, the following terms have been used at least once: correlation coefficients, Cronbach's alpha, hazard ratios, incidence rates, raw mean differences, odds ratios, proportions, ratios of means, raw means, risk differences, risk ratios, (semi-)partial correlations, standardized mean changes, standardized mean differences. In terms of models/methods/concepts, the following terms have been used at least once: cluster-robust inference, component network meta-analysis, cumulative meta-analysis, diagnostic accuracy studies, dose response models, generalized linear models, longitudinal models, Mantel-Haenszel method, meta-regression, model checks, multilevel models, multivariate models, network meta-analysis, outliers, Peto's method, phylogeny, publication bias, reliability generalization, single-arm studies, spatial correlation, subgroup analysis.") cat("\n\n") pattern <- NULL next } if (interactive && identical(pattern, "c")) { concept <- !concept message("Switching to a ", ifelse(concept, "concept term search.", "full-text search.")) pattern <- NULL next } if (interactive && identical(pattern, "m")) { matchall <- !matchall message("Switching to matching of ", ifelse(matchall, "all search terms.", "any search term.")) pattern <- NULL next } if (interactive && identical(pattern, "f")) { fixed <- !fixed message("Switching to ", ifelse(fixed, "fixed string matching.", "use of regular expressions.")) pattern <- NULL next } if (interactive && identical(pattern, "p")) { pkgdown <- !pkgdown message("Switching to showing the ", ifelse(pkgdown, "pkgdown docs.", "standard help files.")) pattern <- NULL next } ######################################################################## # for a concept term search or fixed-term full-text search, can specify a # single pattern separated by "," or ";" or "and" or "AND" which will # automatically be split into separate patterns if (pattern != "" && (concept || fixed)) { pattern <- strsplit(pattern, ",", fixed=TRUE) pattern <- unlist(pattern) pattern <- strsplit(pattern, ";", fixed=TRUE) pattern <- unlist(pattern) pattern <- strsplit(pattern, " and ", fixed=TRUE) pattern <- unlist(pattern) pattern <- strsplit(pattern, " AND ", fixed=TRUE) pattern <- unlist(pattern) pattern <- trimws(pattern) } # number of patterns specified n <- length(pattern) # search for relevant datasets matches <- list() if (concept) { for (i in 1:n) { matches[[i]] <- utils::help.search(pattern[i], package="metadat", fields="concept")$matches } if (pattern == "") matches[[1]] <- matches[[1]][!is.element(matches[[1]]$Name, c("datsearch", "metadat-package", "prep_dat")),] } else { for (i in 1:n) { hits <- try(grep(ifelse(fixed, tolower(pattern[i]), pattern[i]), tmpenv$rdtxt, fixed=fixed), silent=TRUE) # grep for pattern in rdtxt if (inherits(hits, "try-error")) { warning(paste0("Search pattern '", pattern[i], "' is not a valid regular expression that can be searched for."), call.=FALSE) hits <- "none" } else { hits <- names(tmpenv$rdtxt)[hits] # get names of datasets that match if (length(hits) == 0) hits <- "none" } matches[[i]] <- do.call(rbind, lapply(hits, function(x) utils::help.search(x, package="metadat", fields="name")$matches)) } } all.matches <- do.call(rbind, matches) if (matchall) { matches <- all.matches[all.matches$Name %in% Reduce(intersect, lapply(matches, function(x) x$Name)),] } else { matches <- all.matches } # if nothing is found if (is.null(matches) || nrow(matches) == 0) { cat('No results found.\n') if (!interactive) { cat("\n") return(invisible()) } pattern <- NULL next } # keep unique matches matches$Entry <- NULL matches <- unique(matches) # get names and titles of matching datasets names <- matches$Name titles <- matches$Title if (length(names) == 1L) { message("Single match found - showing the help file for this dataset.") # if there is only one match, automatically select this match if (pkgdown) { url <- paste0("https://wviechtb.github.io/metadat/reference/", names[1], ".html") browseURL(url) } else { print(help(names[1], package="metadat")) } } else { # otherwise prompt the user to select one of the matches # shorten titles to avoid line wrapping width <- options("width")$width width.names <- nchar(names) width.titles <- nchar(titles) max.width.titles <- width - max(width.names) - 10 titles <- ifelse(width.titles > max.width.titles, paste0(substr(titles, 1, max.width.titles), "..."), titles) # print names and titles of matches cat("\n") print(data.frame(Name = names, Title = titles), right=FALSE) cat("\n") while (TRUE) { if (interactive) { sel <- readline(prompt = "Choose the number of the dataset you would like to see (or to do a new search): ") } else { sel <- readline(prompt = "Choose the number of the dataset you would like to see (or to exit): ") } if (identical(sel, "p")) { pkgdown <- !pkgdown message("Switching to showing the ", ifelse(pkgdown, "pkgdown docs.", "standard help files.")) next } if (identical(sel, "") || identical(sel, "0")) { cat("\n") if (!interactive) return(invisible()) break } sel <- suppressWarnings(round(as.numeric(sel))) if (is.na(sel)) { message("Must enter a dataset number.") next } if (sel < 1 || sel > length(titles)) { message(paste0("Dataset number must be between 1 and ", length(titles), ".")) next } # show the help file if (pkgdown) { url <- paste0("https://wviechtb.github.io/metadat/reference/", names[sel], ".html") browseURL(url) } else { print(help(names[sel], package="metadat")) } } } pattern <- NULL } } metadat/R/prep_dat.r0000644000176200001440000001246314167070054014065 0ustar liggesusersprep_dat <- function(rebuild=FALSE, overwrite, pkgdir) { cat("\n") # if 'pkgdir' argument is unspecified, assume it is the current working directory if (missing(pkgdir)) { pkgdir <- normalizePath(".") cat("Package root directory:", pkgdir, "\n\n") } # check if package root directory actually exists and that it has # a DESCRIPTION file with "Package: metadat" in the first line if (dir.exists(pkgdir)) { if (!file.exists(paste0(pkgdir, "/DESCRIPTION"))) stop("No DESCRIPTION file in the package root directory.") if (readLines(paste0(pkgdir, "/DESCRIPTION"), n=1) != "Package: metadat") stop("DESCRIPTION file in the package root directory is not for the 'metadat' package.") } else { stop("Specified 'pkgdir' directory does not exist.") } data_raw.dir <- paste0(pkgdir, "/data-raw/") data.dir <- paste0(pkgdir, "/data/") man.dir <- paste0(pkgdir, "/man/") # check that directories actually exist if (!dir.exists(data_raw.dir)) stop("Cannot find 'data-raw' directory.") if (!dir.exists(data.dir)) stop("Cannot find 'data' directory.") if (!dir.exists(man.dir)) stop("Cannot find 'man' directory.") # load .rfiles.txt (if it exists) if (file.exists(paste0(data_raw.dir, ".rfiles.txt"))) { .rfiles <- read.table(paste0(data_raw.dir, ".rfiles.txt"), header=FALSE, as.is=TRUE)[[1]] } else { .rfiles <- NULL } # try running Rscript tmp <- try(suppressWarnings(system2("Rscript", args="-e 1", stdout=TRUE, stderr=TRUE)), silent=TRUE) if (inherits(tmp, "try-error")) stop("Cannot run 'Rscript'. Make sure that Rscript/Rscript.exe is on the system path.") # get names of all data preparation scripts (.r/.R files) in the 'data-raw' directory rfiles <- list.files(path=data_raw.dir, pattern=".[rR]$") # paste header for output cat("File", paste0(rep(" ", max(nchar(rfiles))-4), collapse=""), "new ", "rda ", "build ", "Rd ", "create", "\n") cat(paste0(rep("-", max(nchar(rfiles))+29), collapse=""), "\n") # process all data preparation scripts for (i in seq_along(rfiles)) { # paste name of the data preparation script (and enough space to align everything) cat(rfiles[i], paste0(rep(" ", max(nchar(rfiles))-nchar(rfiles[i])+1), collapse="")) # check if the data preparation file is already in .rfiles.txt rfile.exists <- rfiles[i] %in% .rfiles # [new]: paste T/F if it is a new script or not cat(ifelse(!rfile.exists, "T ", "F ")) # get 'root' name of the data preparation script (remove .r/.R extension) root <- substr(rfiles[i], 1, nchar(rfiles[i])-2) # get names of all .rda files in the 'data' directory rda.files <- list.files(path=data.dir, pattern=".rda$") # grep names of all ..rda files (could be one or multiple) rda.files <- grep(paste0("^", root, ".*.rda"), rda.files, value=TRUE) # check if ..rda files already exist rda.files.exist <- ifelse(length(rda.files) > 0, TRUE, FALSE) # [rda]: paste T/F if the rda files already exist or not cat(ifelse(rda.files.exist, "T ", "F ")) # if rebuild=TRUE or if the rda files do not exist, try running the data preparation script # [build]: paste T/F if the data processing script was run (without error) if (rebuild || !rda.files.exist) { #rfilerun <- try(source(paste0(data_raw.dir, rfiles[i])), silent=TRUE) #if (inherits(rfilerun, "try-error")) { # run each script in its own independent process cmd <- paste0(data_raw.dir, rfiles[i]) out <- suppressWarnings(system2("Rscript", cmd, stdout=TRUE, stderr=TRUE)) if (isTRUE(attributes(out)$status == 1)) { warning("Error while running ", rfiles[i], ".", call.=FALSE) cat("F ") } else { cat("T ") } } else { cat("F ") } # list all files in the 'data' directory (possibly includes non-.rda files) data.files <- list.files(path=data.dir) # check if there are now any non-.rda files in 'data'; if so, throw an error if (any(tools::file_ext(data.files) != "rda")) stop("\n\nThere are non-.rda files in the 'data' directory.\nData preparation scripts must create only .rda files.") # check if .Rd file exists in 'man' directory rd.exists <- file.exists(paste0(man.dir, root, ".Rd")) # [Rd]: paste T/F if .Rd file exists or not if (rd.exists) { cat("T ") } else { cat("F ") } # if it doesn't exist or if it is in 'overwrite' vector, create template .Rd file # [create]: paste T/F if template .Rd file is created if (!rd.exists || !missing(overwrite) && paste0(root, ".Rd") %in% overwrite) { cat("T ") .rd_generator(root, pkgdir, overwrite) } else { cat("F ") } cat("\n") } # make sure every .rda file in 'data' is lower case for (data.file in data.files) { file.rename(paste0(data.dir, data.file), paste0(data.dir, tolower(data.file))) } # write updated .rfiles.txt file to 'data-raw' directory write.table(rfiles, file=paste0(data_raw.dir, ".rfiles.txt"), row.names=FALSE, col.names=FALSE) cat("\n") } metadat/data/0000755000176200001440000000000014750430730012605 5ustar liggesusersmetadat/data/dat.mcdaniel1994.rda0000644000176200001440000000254014167070054016151 0ustar liggesusers՘kPUUE=IJV`H/J:V#{ATTxVһa!?49処[B5Єfكfd:NroљygxnIkc= fL&iKILc8S̚rmUEuVN΅83^6F{1 1深 Ď\v,8N)bNb7 L'SDp*8 &t0LS4p8fsy|2Lp@6@|"p1@. \,0\ WA(ׂy:p=nE [mvP w"w2P*@% bp5,u`) 0,z`9x+(Xcqx<πgs<^/j2x`x փ ԟa#5>GcO!ϧ/:Ʃ̟H-Lt?L\k^j\G$/-Ʃ;II I2N<%-_=ƩyU\j{:I@+s<89<>'>i>>) R;$L}D  })'5X=~gz'5 Fwd8"zWQ;vο`|m~v~/[k_tkW;cŪøT'q:|uc'ພc~CeX=>C^{{|0oO;{9Mh}suS]WVn/*ַ}yPo+.sj/^Ct}No=^hjԾ֏W^C~..pz??Tξh6GbŬ i0<S`QrWA(}Qywc wSm!y腨nATCt@v` Na+g➠Q;{eo89 &v= yvc5_S!Dpy=' U: (1T_W`(. 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LWpS{?$^&taxxjZuO}C{"H`LKKVln\ 2noS0۽;p metadat/README.md0000644000176200001440000001263514746672514013177 0ustar liggesusersmetadat: Meta-Analysis Datasets for R ===================================== [![License: GPL (>=2)](https://img.shields.io/badge/license-GPL-blue)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) [![R build status](https://github.com/wviechtb/metadat/workflows/R-CMD-check/badge.svg)](https://github.com/wviechtb/metadat/actions) [![CRAN Version](https://www.r-pkg.org/badges/version/metadat)](https://cran.r-project.org/package=metadat) [![devel Version](https://img.shields.io/badge/devel-1.5--x-brightgreen.svg)](https://github.com/wviechtb/metadat) [![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/metadat)](https://cran.r-project.org/package=metadat) ## Description The `metadat` package contains a large collection of meta-analysis datasets. These datasets are useful for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. ## Installation The current official (i.e., [CRAN](https://cran.r-project.org/package=metadat)) release can be installed within R with: ```r install.packages("metadat") ``` The development version of the package can be installed with: ```r install.packages("remotes") remotes::install_github("wviechtb/metadat") ``` This builds the package from source based on the current version on [GitHub](https://github.com/wviechtb/metadat). ## Browsing and Searching for Datasets A listing of all datasets in the package can be obtained with `help(package=metadat)`. Each dataset is also tagged with one or multiple concept terms. These concept terms refer to various aspects of a dataset, such as the field/topic of research, the outcome measure used for the analysis, the model(s) used for analyzing the data, and the methods/concepts that can be illustrated with the dataset. The [`datsearch()`](https://wviechtb.github.io/metadat/reference/datsearch.html) function can be used to search among the existing datasets in the package based on their concept terms or based on a full-text search of their corresponding help files. You can also read the documentation online at [https://wviechtb.github.io/metadat/](https://wviechtb.github.io/metadat/) (where the output from the example analyses corresponding to each dataset is provided). ## Contributing New Datasets We welcome contributions of new datasets to the package. For each dataset, there must be a citable reference, ideally in a peer-reviewed journal or publication. The general workflow for contributing a new dataset is as follows: - Install the `metadat` package in R in the usual manner (i.e., `install.packages("metadat")`). - If you are familiar with Git/GitHub and making pull requests, fork the [package repository](https://github.com/wviechtb/metadat). Otherwise, [download](https://github.com/wviechtb/metadat/archive/master.zip) the source version of the package from GitHub and unzip the file to some directory on your computer. - Place the raw data (in a non-binary format) in the `data-raw` directory. The file should be named `dat..`, where `` is the last name of the first author of the publication from which the data come, `` is the publication year, and `` is the file extension (e.g., `.txt`, `.csv`). - Place a corresponding R script in the `data-raw` directory named `dat..r` that reads in the data, possibly does some data cleaning/processing, and then saves the dataset to the `data` directory (using `save()`), with name `dat..rda`. - Start R, load the `metadat` package (i.e., `library(metadat)`), and then run the `prep_dat()` function (either set the working directory to the location of the source package beforehand or use the `pkgdir` argument of the `prep_dat()` function to specify the source package location). - For a new dataset, this should create a boilerplate template for a corresponding help file in the `man` directory, named `dat..Rd`. Edit the help file, adding the title and a short description of the dataset in general, a description of each variable in the dataset, further details on the dataset (e.g., the field of research, how the data was collected, the purpose of the dataset or what it was used for, the effect size or outcome measure used in the analysis, the types of analyses/models that can be illustrated with the dataset), a reference for the source of the dataset, one or multiple concept terms, the name and email address of the contributor of the dataset, and (optionally) example code to illustrate the analysis of the dataset. - Either make a pull request (if you are familiar with this workflow) or zip up the `dat..`, `dat..r`, `dat..rda`, and `dat..Rd` files and open up a new [issue at GitHub](https://github.com/wviechtb/metadat/issues), attaching the zip file. - If the above makes no sense to you, you can also open an issue or email one of the package authors and attach a zip file including a cleaned, raw data file in `.txt` or `.csv` format, along with a meta-data file (format doesn't matter) that includes the information described above. ## Citing the Package If you use these data, please cite both the `metadat` package (see `citation("metadat")` for the reference) and the original source of the data as given under the help file of a dataset. ## Bug/Error Reports If you think you have found an error in an existing dataset or a bug in the package in general, please go to https://github.com/wviechtb/metadat/issues and open up a new issue. metadat/build/0000755000176200001440000000000014750503602012772 5ustar liggesusersmetadat/build/metadat.pdf0000644000176200001440000204165314750503602015117 0ustar liggesusers%PDF-1.5 % 2 0 obj << /Type /ObjStm /N 100 /First 809 /Length 1038 /Filter /FlateDecode >> stream xڍ͒6<]N/- IHUSJ'y٦1e )y4`ib% lHI iG%OR\$ŗ&YY\pIGL)PɢҰB%R &4TVd .-8]hEVXFhї/ŧ>u`:tCe2T:#l34675CxI %#rSp 蝜e'O1M=Χqx1L|y`Us(]ǭT1Me=nI53챉cnZ/;U͑4. 9<#z*fw=GFT= 8;1}?g&"vW3p{!:9S;tLnr~&$/>d1":nSnMq^o 9g2qn caD!}jmI,Fw9MhWF9oUQ/g~2:܎KOP4sy@Gvsm_ =jj mOM "fx{1ݴy]͐c^Oq Ȧp}]Qk/s9^>vMN#9}E\<OdgЅӯպ8,>~+Y1б endstream endobj 402 0 obj << /Length 1120 /Filter /FlateDecode >> stream xY[o6~ϯlIJдI! 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BI ۉݱ0R,KCC# _昂lʦT.N\ OOLONOL LY2N]BCLԽ$u אuC"H}]-+eJGtΛ9!a'v.w9qIɛ~n]Q EγlyS~Ef=r%G^ 1?"1u/CgcR ɀIwY^32Þ {ֳ1U߇ca;|<>, FÜ'/ G$d/'dR bʟazç=782$eR#`2RޑGS@9?0%XbkjmYPZD;G|BZ)iue:i 5-'}*1< <%-[,hzޓKu)~&I42x3ANzկWWWRׁ ?款j sCӯ|H"HygT3Tv2|I}*5'tf}w 0Y.9s暿 1 ?DE6\-tQNҋ P_t9]dSqyU>aV‚G9sڗfw (g# t)?7(E5o)onԵK լ2L!oYbF}S&uNՅNjβf0@-ΓF0' vj[MӨ{iZݝ9vgW=|sHTF`>-ȷGe'Uʔ^~Y|ס'Zq-w>T5^e3w%h3<[Xb }mD1rC?I(r?n朎Fp=.s$ئ>i:{&ad'L!ZSRss,Űg'MxIsȖ^u~GTKG%Y: 릷1I^uZs}9Ƀ_Kz fI[gd6P5CԸ~By;\PYԸ\פ_R^q y"P"7y&Uygq Zh%sE4FdAK1VV Qr32K 'r^-i DH4Z̸ >ɵ/u1.Қ5̺G5OJظ Eִ[ֳTbL[K(%3YKuiuE7`aQNAV!!CDM9-%VMFV"g/#oC~E̳oٷ$~IE,vKz!_HLw%ȱz1!|/b %K@պi8V\dVgHR`g9zRAD{>$G}tbW[Ki9ƒcM6ڪi3W|(}"rKP#ZuEn :>\q?[܇+|=jS ͥwȮy <X!/ 5o gaPDezD OPhDGOu~{ e!Ȯݱ8fecLM߮/4>NN,O OM+؉7A+ Ũt-+5z>zkN\ჽZuG$w?k8V>۾;CXdG]_o(RIS );@џ?Vs+s·5Nvu+vť(/GJE?/n{1FFLzj 甪*T`FPyY%ETY7Ek AޗKH~$$QE @i*,5Y ?&`W5g^vEvRnAJ7[èEG5XuMcQsG# D_:2aJJۗ b˜8Ti2퓓 [oH[Q v@N_Nk-ު rxpL`Zw1YO ?I=Wڢw=IJX(Kꌥuo9ݚ6[K+|h kˊU+7&oߪՍ# >p*F[#jOTP7ut {cَ(S{4% LZk6Οg% `MsծYZaZ}&MɐQ)Qck3rUkW+k=dtoGrڥ*K _wYHʾkQ1=ẜ=UگxCKso#"Y#bBZ܄#Ek`s՚ixĵaи u+܆Uc.߲mʱ(ǓI?% BR֓x^€ >:ZzBځ-S]HblC"ե'1##z(Pވ,q)P <'ي!ϐ* ( kD%zr?xy(̾%tG_ gv[z2> i̐[Ha_ n!kNY.5n!uGr -P/v،MZ6K@)s^vZ?U7yB`%ذ.-|)-;C(@rArlU|}işjls4čs̳3Lոe^AiZ@ g!`%a)}/_jx\7[ܻCey@jf})6Ԧia^sQmoh)ͦV`S((1C)[݊\Ƈ`,_jxB\Ҳi6xCT+0Kz;ĺXEQ4m\Wv Kc 9C #"@~'q->}[Վ8B&smKl0v&P2D{<$#>&mԎy[DkmB0q v <Pn! mp%Ib2[L>u.w^5]Spl׮Ȏ3uGw ?*ܻU{ ޸ v =#4糼AgE5#6 Lì]qMiPXKoHqblp a"s0qD@>Gx:n.l+a~ss\b>T2]eȽcf6Txʼn:=KeXv0}׾J;cIe8 ,'OT F/qlYm+W5Sko.F91vOmetadat/man/0000755000176200001440000000000014750466700012455 5ustar liggesusersmetadat/man/dat.hannum2020.Rd0000644000176200001440000000674414750466700015320 0ustar liggesusers\name{dat.hannum2020} \docType{data} \alias{dat.hannum2020} \title{Studies Comparing Objective and Subjective Olfactory Loss in COVID-19 Patients} \description{Results from 35 studies measuring olfactory loss in COVID-19 patients using either objective or subjective measures.} \usage{ dat.hannum2020 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{authorName} \tab \code{character} \tab (first) author of study \cr \bold{DOI} \tab \code{character} \tab article DOI number \cr \bold{ni} \tab \code{numeric} \tab number of Covid-19 positive patients in the study \cr \bold{xi} \tab \code{numeric} \tab number of Covid-19 positive patients in the study with olfactory loss \cr \bold{percentOlfactoryLoss} \tab \code{numeric} \tab percent of the sample with olfactory loss \cr \bold{objectivity} \tab \code{character} \tab objective or subjective measure used \cr \bold{measured} \tab \code{character} \tab outcome measure \cr \bold{testType} \tab \code{character} \tab type of test used \cr \bold{country} \tab \code{character} \tab country where patients were treated \cr \bold{patientType} \tab \code{character} \tab type of patient information and location where being treated } } \details{ One of the symptoms of COVID-19 infection is olfactory loss (loss of smell) either recently acquired anosmia (complete loss of smell) or hyposmia (partial loss of smell). One challenge to reaching this symptom is the wide range of reported prevalence for this symptom ranging from 5 percent to 98 percent. In this dataset studies were grouped into one of two groups based on the type of method used to measure smell loss (either subjective measures, such as self-reported smell loss, or objective measures using rated stimuli). } \source{ Ramirez VA , Hannum ME, Lipson SJ, Herriman RD, Toskala AK, Lin C, Joseph PV, Reed DR. 2020. COVID-19 Smell Loss Prevalence Tracker. Available from: \verb{https://vicente-ramirez.shinyapps.io/COVID19_Olfactory_Dashboard/} and \verb{https://github.com/vramirez4/OlfactoryLoss} (accessed August 11, 2021) } \references{ Hannum, M. E., Ramirez, V. A., Lipson, S. J., Herriman, R. D., Toskala, A. K., Lin, C., Joseph, P. V., & Reed, D. R. (2020). Objective sensory testing methods reveal a higher prevalence of olfactory loss in COVID-19 positive patients compared to subjective methods: A systematic review and meta-analysis. \emph{Chemical Senses}, \bold{45}(9), 865--874. \verb{https://doi.org/10.1093/chemse/bjaa064} } \author{ W. Kyle Hamilton \email{whamilton@ucmerced.edu} \url{https://kylehamilton.com} } \examples{ # copy data into 'dat' and examine data dat <- dat.hannum2020 dat \dontrun{ # load metafor package library(metafor) # compute effect size dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat) # split data into objective and subjective datasets dat_split <- split(dat, dat$objectivity) dat_objective <- dat_split[["Objective"]] dat_subjective <- dat_split[["Subjective"]] # random-effects model all studies res_all <- rma(yi, vi, data=dat) print(res_all, digits=2) # random-effects model objective res_objective <- rma(yi, vi, data=dat_objective) print(res_objective, digits=2) # random-effects model subjective res_subjective <- rma(yi, vi, data=dat_subjective) print(res_subjective, digits=2) } } \keyword{datasets} \concept{medicine} \concept{covid-19} \concept{proportions} \section{Concepts}{ medicine, covid-19, proportions } metadat/man/dat.fine1993.Rd0000644000176200001440000001422614750466700014767 0ustar liggesusers\name{dat.fine1993} \docType{data} \alias{dat.fine1993} \title{Studies on Radiation Therapy with or without Adjuvant Chemotherapy in Patients with Malignant Gliomas} \description{Results from 17 trials comparing post-operative radiation therapy with and without adjuvant chemotherapy in patients with malignant gliomas.} \usage{ dat.fine1993 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{nei} \tab \code{numeric} \tab sample size in the experimental group receiving radiotherapy plus adjuvant chemotherapy \cr \bold{nci} \tab \code{numeric} \tab sample size in the control group receiving radiotherapy alone \cr \bold{e1i} \tab \code{numeric} \tab number of survivors at 6 months in the experimental group \cr \bold{c1i} \tab \code{numeric} \tab number of survivors at 6 months in the control group \cr \bold{e2i} \tab \code{numeric} \tab number of survivors at 12 months in the experimental group \cr \bold{c2i} \tab \code{numeric} \tab number of survivors at 12 months in the control group \cr \bold{e3i} \tab \code{numeric} \tab number of survivors at 18 months in the experimental group \cr \bold{c3i} \tab \code{numeric} \tab number of survivors at 18 months in the control group \cr \bold{e4i} \tab \code{numeric} \tab number of survivors at 24 months in the experimental group \cr \bold{c4i} \tab \code{numeric} \tab number of survivors at 24 months in the control group } } \details{ The 17 trials report the post-operative survival of patients with malignant gliomas receiving either radiation therapy with adjuvant chemotherapy or radiation therapy alone. Survival was assessed at 6, 12, 18, and 24 months in all but one study (which assessed survival only at 12 and at 24 months). The data were reconstructed by Trikalinos and Olkin (2012) based on Table 2 in Fine et al. (1993) and Table 3 in Dear (1994). The data can be used to illustrate how a meta-analysis can be conducted of effect sizes reported at multiple time points using a multivariate model. } \source{ Dear, K. B. G. (1994). Iterative generalized least squares for meta-analysis of survival data at multiple times. \emph{Biometrics}, \bold{50}(4), 989--1002. \verb{https://doi.org/10.2307/2533438} Trikalinos, T. A., & Olkin, I. (2012). Meta-analysis of effect sizes reported at multiple time points: A multivariate approach. \emph{Clinical Trials}, \bold{9}(5), 610--620. \verb{https://doi.org/10.1177/1740774512453218} } \references{ Fine, H. A., Dear, K. B., Loeffler, J. S., Black, P. M., & Canellos, G. P. (1993). Meta-analysis of radiation therapy with and without adjuvant chemotherapy for malignant gliomas in adults. \emph{Cancer}, \bold{71}(8), 2585--2597. \verb{https://doi.org/10.1002/1097-0142(19930415)71:8<2585::aid-cncr2820710825>3.0.co;2-s} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.fine1993 dat \dontrun{ ### load metafor package library(metafor) ### calculate log(ORs) and sampling variances for each time point dat <- escalc(measure="OR", ai=e1i, n1i=nei, ci=c1i, n2i=nci, data=dat, var.names=c("y1i","v1i")) dat <- escalc(measure="OR", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat, var.names=c("y2i","v2i")) dat <- escalc(measure="OR", ai=e3i, n1i=nei, ci=c3i, n2i=nci, data=dat, var.names=c("y3i","v3i")) dat <- escalc(measure="OR", ai=e4i, n1i=nei, ci=c4i, n2i=nci, data=dat, var.names=c("y4i","v4i")) ### calculate the covariances (equations in Appendix of Trikalinos & Olkin, 2012) dat$v12i <- with(dat, nei / (e1i * (nei - e2i)) + nci / (c1i * (nci - c2i))) dat$v13i <- with(dat, nei / (e1i * (nei - e3i)) + nci / (c1i * (nci - c3i))) dat$v14i <- with(dat, nei / (e1i * (nei - e4i)) + nci / (c1i * (nci - c4i))) dat$v23i <- with(dat, nei / (e2i * (nei - e3i)) + nci / (c2i * (nci - c3i))) dat$v24i <- with(dat, nei / (e2i * (nei - e4i)) + nci / (c2i * (nci - c4i))) dat$v34i <- with(dat, nei / (e3i * (nei - e4i)) + nci / (c3i * (nci - c4i))) ### create dataset in long format dat.long <- data.frame(study=rep(1:nrow(dat), each=4), time=1:4, yi=c(t(dat[c("y1i","y2i","y3i","y4i")])), vi=c(t(dat[c("v1i","v2i","v3i","v4i")]))) ### var-cov matrices of the studies V <- lapply(split(dat, dat$study), function(x) matrix(c( x$v1i, x$v12i, x$v13i, x$v14i, x$v12i, x$v2i, x$v23i, x$v24i, x$v13i, x$v23i, x$v3i, x$v34i, x$v14i, x$v24i, x$v34i, x$v4i), nrow=4, ncol=4, byrow=TRUE)) ### remove rows for the missing time points in study 17 dat.long <- na.omit(dat.long) ### remove corresponding rows/columns from var-cov matrix V[[17]] <- V[[17]][c(2,4),c(2,4)] ### make a copy of V Vc <- V ### replace any (near) singular var-cov matrices with ridge corrected versions repl.Vi <- function(Vi) { res <- eigen(Vi) if (any(res$values <= 0.08)) { round(res$vectors \%*\% diag(res$values + 0.08) \%*\% t(res$vectors), 12) } else { Vi } } Vc <- lapply(Vc, repl.Vi) ### do not correct var-cov matrix of study 17 Vc[[17]] <- V[[17]] ### construct block diagonal matrix Vc <- bldiag(Vc) ### multivariate fixed-effects model res <- rma.mv(yi, Vc, mods = ~ 0 + factor(time), method="FE", data=dat.long) print(res, digits=3) ### multivariate random-effects model with heteroscedastic AR(1) structure for the true effects res <- rma.mv(yi, Vc, mods = ~ 0 + factor(time), random = ~ time | study, struct="HAR", data=dat.long, control=list(optimizer="hjk")) print(res, digits=3) ### profile the variance components par(mfrow=c(2,2)) profile(res, tau2=1, xlim=c( 0, 0.2)) profile(res, tau2=2, xlim=c( 0, 0.2)) profile(res, tau2=3, xlim=c( 0, 0.2)) profile(res, tau2=4, xlim=c(0.1, 0.3)) ### profile the autocorrelation coefficient par(mfrow=c(1,1)) profile(res, rho=1) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{odds ratios} \concept{longitudinal models} \section{Concepts}{ medicine, oncology, odds ratios, longitudinal models } metadat/man/dat.collins1985a.Rd0000644000176200001440000001026514750466700015652 0ustar liggesusers\name{dat.collins1985a} \docType{data} \alias{dat.collins1985a} \title{Studies on the Treatment of Upper Gastrointestinal Bleeding by a Histamine H2 Antagonist} \description{Results from studies examining the effectiveness of histamine H2 antagonists (cimetidine or ranitidine) in treating patients with acute upper gastrointestinal hemorrhage.} \usage{ dat.collins1985a } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study number \cr \bold{trial} \tab \code{character} \tab first author of trial \cr \bold{year} \tab \code{numeric} \tab year of publication \cr \bold{ref} \tab \code{numeric} \tab reference number \cr \bold{trt} \tab \code{character} \tab C = cimetidine, R = ranitidine \cr \bold{ctrl} \tab \code{character} \tab P = placebo, AA = antacids, UT = usual treatment \cr \bold{nti} \tab \code{numeric} \tab number of patients in treatment group \cr \bold{b.xti} \tab \code{numeric} \tab number of patients in treatment group with persistent or recurrent bleedings \cr \bold{o.xti} \tab \code{numeric} \tab number of patients in treatment group in need of operation \cr \bold{d.xti} \tab \code{numeric} \tab number of patients in treatment group that died \cr \bold{nci} \tab \code{numeric} \tab number of patients in control group \cr \bold{b.xci} \tab \code{numeric} \tab number of patients in control group with persistent or recurrent bleedings \cr \bold{o.xci} \tab \code{numeric} \tab number of patients in control group in need of operation \cr \bold{d.xci} \tab \code{numeric} \tab number of patients in control group that died } } \details{ The data were obtained from Tables 1 and 2 in Collins and Langman (1985). The authors used Peto's (one-step) method for meta-analyzing the 27 trials. This approach is implemented in the \code{\link[metafor]{rma.peto}} function. Using the same dataset, van Houwelingen, Zwinderman, and Stijnen (1993) describe some alternative approaches for analyzing these data, including fixed- and random-effects conditional logistic models. Those are implemented in the \code{\link[metafor]{rma.glmm}} function. } \source{ Collins, R., & Langman, M. (1985). Treatment with histamine H2 antagonists in acute upper gastrointestinal hemorrhage. \emph{New England Journal of Medicine}, \bold{313}(11), 660--666. \verb{https://doi.org/10.1056/NEJM198509123131104} } \references{ van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. \emph{Statistics in Medicine}, \bold{12}(24), 2273--2284. \verb{https://doi.org/10.1002/sim.4780122405} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.collins1985a dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of log ORs using Peto's method (outcome: persistent or recurrent bleedings) res <- rma.peto(ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat) print(res, digits=2) ### meta-analysis of log ORs using a conditional logistic regression model (FE model) res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat, model="CM.EL", method="FE") summary(res) predict(res, transf=exp, digits=2) ### plot the likelihoods of the odds ratios llplot(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat, lwd=1, refline=NA, xlim=c(-4,4), drop00=FALSE) ### meta-analysis of log odds ratios using a conditional logistic regression model (RE model) res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat, model="CM.EL", method="ML") summary(res) predict(res, transf=exp, digits=2) ### meta-analysis of log ORs using Peto's method (outcome: need for surgery) res <- rma.peto(ai=o.xti, n1i=nti, ci=o.xci, n2i=nci, data=dat) print(res, digits=2) ### meta-analysis of log ORs using Peto's method (outcome: death) res <- rma.peto(ai=d.xti, n1i=nti, ci=d.xci, n2i=nci, data=dat) print(res, digits=2) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{Peto's method} \concept{generalized linear models} \section{Concepts}{ medicine, odds ratios, Peto's method, generalized linear models } metadat/man/dat.laopaiboon2015.Rd0000644000176200001440000000576214750466700016160 0ustar liggesusers\name{dat.laopaiboon2015} \docType{data} \alias{dat.laopaiboon2015} \title{Studies on the Effectiveness of Azithromycin for Treating Lower Respiratory Tract Infections} \description{Results from 15 studies on the effectiveness of azithromycin versus amoxycillin or amoxycillin/clavulanic acid (amoxyclav) in the treatment of acute lower respiratory tract infections.} \usage{ dat.laopaiboon2015 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ai} \tab \code{numeric} \tab number of clinical failures in the group treated with azithromycin \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the group treated with azithromycin \cr \bold{ci} \tab \code{numeric} \tab number of clinical failures in the group treated with amoxycillin or amoxyclav \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the group treated with amoxycillin or amoxyclav \cr \bold{age} \tab \code{character} \tab whether the trial included adults or children \cr \bold{diag.ab} \tab \code{numeric} \tab trial included patients with a diagnosis of acute bacterial bronchitis \cr \bold{diag.cb} \tab \code{numeric} \tab trial included patients with a diagnosis of chronic bronchitis with acute exacerbation \cr \bold{diag.pn} \tab \code{numeric} \tab trial included patients with a diagnosis of pneumonia \cr \bold{ctrl} \tab \code{character} \tab antibiotic in control group (amoxycillin or amoxyclav) } } \details{ Azithromycin is an antibiotic useful for the treatment of a number of bacterial infections. Laopaiboon et al. (2015) conducted a meta-analysis of trials comparing the effectiveness of azithromycin versus amoxycillin or amoxycillin/clavulanic acid (amoxyclav) in the treatment of acute lower respiratory tract infections, including acute bacterial bronchitis, acute exacerbations of chronic bronchitis, and pneumonia. The results from 15 trials are included in this dataset. } \source{ Laopaiboon, M., Panpanich, R., & Swa Mya, K. (2015). Azithromycin for acute lower respiratory tract infections. \emph{Cochrane Database of Systematic Reviews}, \bold{3}, CD001954. \verb{https://doi.org/10.1002/14651858.CD001954.pub4} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.laopaiboon2015 dat \dontrun{ ### load metafor package library(metafor) ### analysis using the Mantel-Haenszel method rma.mh(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, digits=3) ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat) ### random-effects model res <- rma(yi, vi, data=dat) res ### average risk ratio with 95\% CI predict(res, transf=exp) } } \keyword{datasets} \concept{medicine} \concept{risk ratios} \section{Concepts}{ medicine, risk ratios } metadat/man/dat.linde2016.Rd0000644000176200001440000001064614750466700015126 0ustar liggesusers\name{dat.linde2016} \docType{data} \alias{dat.linde2016} \title{Studies on Antidepressants for the Primary Care Setting} \description{Results from 93 trials examining 22 interventions (including placebo and usual care) for the primary care of depression.} \usage{ dat.linde2016 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{integer} \tab study ID \cr \bold{lnOR} \tab \code{numeric} \tab response after treatment (log odds ratio) \cr \bold{selnOR} \tab \code{numeric} \tab standard error of log odds ratio \cr \bold{treat1} \tab \code{character} \tab first treatment \cr \bold{treat2} \tab \code{character} \tab second treatment } } \details{ This dataset comes from a network meta-analysis of 22 treatments of depression in primary care (Linde et al., 2016), based on 93 trials (79 two-arm trials, 13 three-arm trials, and one four-arm trial). The primary outcome was response after treatment (yes/no), defined as a reduction from baseline by at least 50\% on a depression scale. The dataset contains log odds ratios with standard errors for all pairwise comparisons. The interventions comprised both medical and psychological treatments, also in combination, including placebo and usual care (UC) (Linde et al., 2016). Pharmacological interventions were tricyclic antidepressants (TCA), selective serotonin reuptake inhibitors (SSRI), serotonin-noradrenaline reuptake inhibitors (SNRI), noradrenaline reuptake inhibitors (NRI), low- dose serotonin (5-HT2) antagonists and reuptake inhibitors (low-dose SARI), noradrenergic and specific serotonergic agents (NaSSa), reversible inhibitors of monoaminoxidase A (rMAO-A), hypericum extracts, and an individualized drug. Psychological interventions were cognitive behavioral therapy (CBT; four forms: face-to-face CBT, remote therapist-led CBT, guided self-help CBT, and no or minimal contact CBT), face-to-face problem-solving therapy (PST), face-to-face interpersonal psychotherapy, face-to-face psychodynamic therapy, and \dQuote{other face-to-face therapy}. Combination therapies were face-to-face CBT + SSRI, face-to-face PST + SSRI, and face-to-face interpersonal psychotherapy + SSRI. The dataset was used as an example in Rücker et al. (2020) to illustrate component network meta-analysis using frequentist methods. } \source{ Linde, K., Rücker, G., Schneider, A., & Kriston, L. (2016). Questionable assumptions hampered interpretation of a network meta-analysis of primary care depression treatments. \emph{Journal of Clinical Epidemiology}, \bold{71}, 86--96. \verb{https://doi.org/10.1016/j.jclinepi.2015.10.010} } \references{ Rücker, G., Petropoulou, M., & Schwarzer, G. (2020). Network meta-analysis of multicomponent interventions. \emph{Biometrical Journal}, \bold{62}(3), 808--821. \verb{https://doi.org/10.1002/bimj.201800167} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show results of first three studies (first study has three treatment ### arms) head(dat.linde2016, 5) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Define order of treatments in printouts and forest plots trts <- c("SSRI", "Face-to-face CBT", "Face-to-face interpsy", "Face-to-face PST", "Face-to-face CBT + SSRI", "Face-to-face interpsy + SSRI", "Face-to-face PST + SSRI", "Face-to-face psychodyn", "Other face-to-face", "TCA", "SNRI", "NRI", "Low-dose SARI", "NaSSa", "rMAO-A", "Ind drug", "Hypericum", "Remote CBT", "Self-help CBT", "No contact CBT", "UC", "Placebo") ### Conduct random effects network meta-analysis net <- netmeta(lnOR, selnOR, treat1, treat2, id, data = dat.linde2016, reference.group = "placebo", seq = trts, sm = "OR", fixed = FALSE) ### Network graph netgraph(net, seq = "o", number = TRUE) ### Show results net forest(net, xlim = c(0.2, 50)) ### Additive component network meta-analysis with placebo as inactive ### treatment nc <- netcomb(net, inactive = "placebo") nc forest(nc, xlim = c(0.2, 50)) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{psychiatry} \concept{odds ratios} \concept{network meta-analysis} \concept{component network meta-analysis} \section{Concepts}{ medicine, psychiatry, odds ratios, network meta-analysis, component network meta-analysis } metadat/man/dat.begg1989.Rd0000644000176200001440000000761314750466700014761 0ustar liggesusers\name{dat.begg1989} \docType{data} \alias{dat.begg1989} \title{Studies on Bone-Marrow Transplantation versus Chemotherapy for the Treatment of Leukemia} \description{Results from controlled and uncontrolled studies on the effectiveness of allogeneic bone-marrow transplantation (BMT) and conventional chemotherapy (CMO) in the treatment of acute nonlymphocytic leukemia.} \usage{ dat.begg1989 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{trt} \tab \code{character} \tab treatment (BMT or CMO) \cr \bold{arms} \tab \code{numeric} \tab number of arms in the study (1 = uncontrolled studies; 2 = controlled studies) \cr \bold{yi} \tab \code{numeric} \tab 2-year disease-free survival rates \cr \bold{sei} \tab \code{numeric} \tab corresponding standard errors \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variances } } \details{ The dataset includes the results from controlled and uncontrolled studies on the 2-year disease-free survival rate in patients with acute nonlymphocytic leukemia receiving either allogeneic bone-marrow transplantation (BMT) or conventional chemotherapy (CMO). In the controlled (two-arm) studies (studies 1-4), a cohort of patients in complete remission and potentially eligible for BMT was assembled, and those who consented and for whom a donor could be found received BMT, with the remaining patients used as controls (receiving CMO). In the uncontrolled (one-arm) studies (studies 5-16), only a single group was studied, receiving either BMT or CMO. The data in this dataset were obtained from Table 1 in Begg and Pilote (1991, p. 902). } \source{ Begg, C. B., & Pilote, L. (1991). A model for incorporating historical controls into a meta-analysis. \emph{Biometrics}, \bold{47}(3), 899--906. \verb{https://doi.org/10.2307/2532647} } \references{ Begg, C. B., Pilote, L., & McGlave, P. B. (1989). Bone marrow transplantation versus chemotherapy in acute non-lymphocytic leukemia: A meta-analytic review. \emph{European Journal of Cancer and Clinical Oncology}, \bold{25}(11), 1519--1523. \verb{https://doi.org/10.1016/0277-5379(89)90291-5} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.begg1989 dat \dontrun{ ### load metafor package library(metafor) ### turn trt and arms into factors and set reference levels dat$trt <- relevel(factor(dat$trt), ref="CMO") dat$arms <- relevel(factor(dat$arms), ref="2") ### create data frame with the treatment differences for the controlled studies dat2 <- data.frame(yi = dat$yi[c(1,3,5,7)] - dat$yi[c(2,4,6,8)], vi = dat$vi[c(1,3,5,7)] + dat$vi[c(2,4,6,8)]) dat2 ### DerSimonian and Laird method using the treatment differences res <- rma(yi, vi, data=dat2, method="DL", digits=2) res ### Begg & Pilote (1991) model incorporating the uncontrolled studies res <- rma.mv(yi, vi, mods = ~ trt, random = ~ 1 | study, data=dat, method="ML", digits=2) res ### model involving bias terms for the uncontrolled studies res <- rma.mv(yi, vi, mods = ~ trt + trt:arms, random = ~ 1 | study, data=dat, method="ML", digits=2) res ### model with a random treatment effect res <- rma.mv(yi, vi, mods = ~ trt, random = list(~ 1 | study, ~ trt | study), struct="UN", tau2=c(0,NA), rho=0, data=dat, method="ML", digits=2) res ### model with a random treatment effect, but with equal variances in both arms res <- rma.mv(yi, vi, mods = ~ trt, random = list(~ 1 | study, ~ trt | study), struct="CS", rho=0, data=dat, method="ML", digits=2) res } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{single-arm studies} \concept{multilevel models} \section{Concepts}{ medicine, oncology, single-arm studies, multilevel models } metadat/man/dat.linde2015.Rd0000644000176200001440000001342314750466700015121 0ustar liggesusers\name{dat.linde2015} \docType{data} \alias{dat.linde2015} \title{Studies on Classes of Antidepressants for the Primary Care Setting} \description{Results from 66 trials examining eight classes of antidepressants and placebo for the primary care setting.} \usage{ dat.linde2015 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{integer} \tab study ID \cr \bold{author} \tab \code{character} \tab first author \cr \bold{year} \tab \code{integer} \tab year of publication \cr \bold{treatment1} \tab \code{character} \tab treatment 1 \cr \bold{treatment2} \tab \code{character} \tab treatment 2 \cr \bold{treatment3} \tab \code{character} \tab treatment 3 \cr \bold{n1} \tab \code{integer} \tab number of patients (arm 1) \cr \bold{resp1} \tab \code{integer} \tab number of early responder (arm 1) \cr \bold{remi1} \tab \code{integer} \tab number of early remissions (arm 1) \cr \bold{loss1} \tab \code{integer} \tab number of patients loss to follow-up (arm 1) \cr \bold{loss.ae1} \tab \code{integer} \tab number of patients loss to follow-up due to adverse events (arm 1) \cr \bold{ae1} \tab \code{integer} \tab number of patients with adverse events (arm 1) \cr \bold{n2} \tab \code{integer} \tab number of patients (arm 2) \cr \bold{resp2} \tab \code{integer} \tab number of early responder (arm 2) \cr \bold{remi2} \tab \code{integer} \tab number of early remissions (arm 2) \cr \bold{loss2} \tab \code{integer} \tab number of patients loss to follow-up (arm 2) \cr \bold{loss.ae2} \tab \code{integer} \tab number of patients loss to follow-up due to adverse events (arm 2) \cr \bold{ae2} \tab \code{integer} \tab number of patients with adverse events (arm 2) \cr \bold{n3} \tab \code{integer} \tab number of patients (arm 3) \cr \bold{resp3} \tab \code{integer} \tab number of early responder (arm 3) \cr \bold{remi3} \tab \code{integer} \tab number of early remissions (arm 3) \cr \bold{loss3} \tab \code{integer} \tab number of patients loss to follow-up (arm 3) \cr \bold{loss.ae3} \tab \code{integer} \tab number of patients loss to follow-up due to adverse events (arm 3) \cr \bold{ae3} \tab \code{integer} \tab number of patients with adverse events (arm 3) } } \details{ This dataset comes from a systematic review of 8 pharmacological treatments of depression and placebo in primary care with 66 studies (8 of which were 3-arm studies) including 14,785 patients. The primary outcome is early response, defined as at least a 50\% score reduction on a depression scale after completion of treatment. Secondary outcomes (also measured as dichotomous) were early remission (defined as having a symptom score below a fixed threshold after completion of treatment), lost to follow-up, lost to follow-up due to adverse events, and any adverse event. The odds ratio was used as effect measure. This dataset was used as an example in Rücker and Schwarzer (2017) who introduced methods to resolve conflicting rankings of outcomes in network meta-analysis. } \source{ Linde, K., Kriston, L., Rücker, G., Jamil, S., Schumann, I., Meissner, K., Sigterman, K., & Schneider, A. (2015). Efficacy and acceptability of pharmacological treatments for depressive disorders in primary care: Systematic review and network meta-analysis. \emph{Annals of Family Medicine}, \bold{13}(1), 69--79. \verb{https://doi.org/10.1370/afm.1687} } \references{ Rücker, G., & Schwarzer, G. (2017). Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. \emph{Research Synthesis Methods}, \bold{8}(4), 526--536. \verb{https://doi.org/10.1002/jrsm.1270} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show results from first three studies (including three-arm study ### Lecrubier 1997) head(dat.linde2015, 3) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Change appearance of confidence intervals cilayout("(", "-") ### Define order of treatments in printouts trts <- c("TCA", "SSRI", "SNRI", "NRI", "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum", "Placebo") ### Transform data from wide arm-based format to contrast-based format ### (outcome: early response). Argument 'sm' has to be used for odds ### ratio as summary measure; by default the risk ratio is used in the ### metabin function called internally. pw1 <- pairwise(list(treatment1, treatment2, treatment3), event = list(resp1, resp2, resp3), n = list(n1, n2, n3), studlab = id, data = dat.linde2015, sm = "OR") ### Conduct random effects network meta-analysis for primary outcome ### (early response); small number of early responses is bad (argument ### small.values) net1 <- netmeta(pw1, fixed = FALSE, reference = "Placebo", seq = trts, small.values = "bad") net1 ### Random effects NMA for early remission pw2 <- pairwise(treat = list(treatment1, treatment2, treatment3), event = list(remi1, remi2, remi3), n = list(n1, n2, n3), studlab = id, data = dat.linde2015, sm = "OR") net2 <- netmeta(pw2, fixed = FALSE, seq = trts, ref = "Placebo", small.values = "bad") net2 ### Ranking of treatments nr1 <- netrank(net1) nr2 <- netrank(net2) nr1 nr2 ### Partial order of treatment rankings (two outcomes) outcomes <- c("Early response", "Early remission") po12 <- netposet(nr1, nr2, outcomes = outcomes) plot(po12) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{psychiatry} \concept{odds ratios} \concept{network meta-analysis} \section{Concepts}{ medicine, psychiatry, odds ratios, network meta-analysis } metadat/man/dat.bcg.Rd0000644000176200001440000001017714750466700014254 0ustar liggesusers\name{dat.bcg} \docType{data} \alias{dat.bcg} \title{Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis} \description{Results from 13 studies examining the effectiveness of the Bacillus Calmette-Guerin (BCG) vaccine against tuberculosis. \loadmathjax} \usage{ dat.bcg } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{numeric} \tab trial number \cr \bold{author} \tab \code{character} \tab author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{tpos} \tab \code{numeric} \tab number of TB positive cases in the treated (vaccinated) group \cr \bold{tneg} \tab \code{numeric} \tab number of TB negative cases in the treated (vaccinated) group \cr \bold{cpos} \tab \code{numeric} \tab number of TB positive cases in the control (non-vaccinated) group \cr \bold{cneg} \tab \code{numeric} \tab number of TB negative cases in the control (non-vaccinated) group \cr \bold{ablat} \tab \code{numeric} \tab absolute latitude of the study location (in degrees) \cr \bold{alloc} \tab \code{character} \tab method of treatment allocation (random, alternate, or systematic assignment) } } \details{ The 13 studies provide data in terms of \mjeqn{2 \times 2}{2x2} tables in the form: \tabular{lcc}{ \tab TB positive \tab TB negative \cr vaccinated group \tab \code{tpos} \tab \code{tneg} \cr control group \tab \code{cpos} \tab \code{cneg} } The goal of the meta-analysis was to examine the overall effectiveness of the BCG vaccine for preventing tuberculosis and to examine moderators that may potentially influence the size of the effect. The dataset has been used in several publications to illustrate meta-analytic methods (see \sQuote{References}). } \source{ Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., & Mosteller, F. (1994). Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. \emph{Journal of the American Medical Association}, \bold{271}(9), 698--702. \verb{https://doi.org/10.1001/jama.1994.03510330076038} } \references{ Berkey, C. S., Hoaglin, D. C., Mosteller, F., & Colditz, G. A. (1995). A random-effects regression model for meta-analysis. \emph{Statistics in Medicine}, \bold{14}(4), 395--411. \verb{https://doi.org/10.1002/sim.4780140406} van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. \emph{Statistics in Medicine}, \bold{21}(4), 589--624. \verb{https://doi.org/10.1002/sim.1040} Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. \emph{Journal of Statistical Software}, \bold{36}(3), 1--48. \verb{https://doi.org/10.18637/jss.v036.i03} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bcg dat \dontrun{ ### load metafor package library(metafor) ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat, slab=paste0(author, ", ", year)) dat ### random-effects model res <- rma(yi, vi, data=dat) res ### average risk ratio with 95\% CI predict(res, transf=exp) ### mixed-effects model with absolute latitude and publication year as moderators res <- rma(yi, vi, mods = ~ ablat + year, data=dat) res ### predicted average risk ratios for 10-60 degrees absolute latitude ### holding the publication year constant at 1970 predict(res, newmods=cbind(seq(from=10, to=60, by=10), 1970), transf=exp) ### note: the interpretation of the results is difficult because absolute ### latitude and publication year are strongly correlated (the more recent ### studies were conducted closer to the equator) plot(ablat ~ year, data=dat, pch=19, xlab="Publication Year", ylab="Absolute Lattitude") cor(dat$ablat, dat$year) } } \keyword{datasets} \concept{medicine} \concept{risk ratios} \concept{meta-regression} \section{Concepts}{ medicine, risk ratios, meta-regression } metadat/man/dat.baskerville2012.Rd0000644000176200001440000001020214750466700016316 0ustar liggesusers\name{dat.baskerville2012} \docType{data} \alias{dat.baskerville2012} \title{Studies on the Effectiveness of Practice Facilitation Interventions} \description{Results from 23 studies on the effectiveness of practice facilitation interventions within the primary care practice setting.} \usage{ dat.baskerville2012 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{score} \tab \code{numeric} \tab quality score (0 to 12 scale) \cr \bold{design} \tab \code{character} \tab study design (cct = controlled clinical trial, rct = randomized clinical trial, crct = cluster randomized clinical trial) \cr \bold{alloconc} \tab \code{numeric} \tab allocation concealed (0 = no, 1 = yes) \cr \bold{blind} \tab \code{numeric} \tab single- or double-blind study (0 = no, 1 = yes) \cr \bold{itt} \tab \code{numeric} \tab intention to treat analysis (0 = no, 1 = yes) \cr \bold{fumonths} \tab \code{numeric} \tab follow-up months \cr \bold{retention} \tab \code{numeric} \tab retention (in percent) \cr \bold{country} \tab \code{character} \tab country where study was conducted \cr \bold{outcomes} \tab \code{numeric} \tab number of outcomes assessed \cr \bold{duration} \tab \code{numeric} \tab duration of intervention \cr \bold{pperf} \tab \code{numeric} \tab practices per facilitator \cr \bold{meetings} \tab \code{numeric} \tab (average) number of meetings \cr \bold{hours} \tab \code{numeric} \tab (average) hours per meeting \cr \bold{tailor} \tab \code{numeric} \tab intervention tailored to the context and needs of the practice (0 = no, 1 = yes) \cr \bold{smd} \tab \code{numeric} \tab standardized mean difference \cr \bold{se} \tab \code{numeric} \tab corresponding standard error } } \details{ Baskerville et al. (2012) describe outreach or practice facilitation as a "multifaceted approach that involves skilled individuals who enable others, through a range of intervention components and approaches, to address the challenges in implementing evidence-based care guidelines within the primary care setting". The studies included in this dataset examined the effectiveness of practice facilitation interventions for improving some relevant evidence-based practice behavior. The effect was quantified in terms of a standardized mean difference, comparing the change (from pre- to post-intervention) in the intervention versus the comparison group (or the difference from baseline in prospective cohort studies). } \source{ Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. \emph{Annals of Family Medicine}, \bold{10}(1), 63--74. \verb{https://doi.org/10.1370/afm.1312} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.baskerville2012 dat \dontrun{ ### load metafor package library(metafor) ### random-effects model res <- rma(smd, sei=se, data=dat, method="DL") print(res, digits=2) ### funnel plot funnel(res, xlab="Standardized Mean Difference", ylim=c(0,0.6)) ### rank and regression tests for funnel plot asymmetry ranktest(res) regtest(res) ### meta-regression analyses examining various potential moderators rma(smd, sei=se, mods = ~ score, data=dat, method="DL") rma(smd, sei=se, mods = ~ alloconc, data=dat, method="DL") rma(smd, sei=se, mods = ~ blind, data=dat, method="DL") rma(smd, sei=se, mods = ~ itt, data=dat, method="DL") rma(smd, sei=se, mods = ~ duration, data=dat, method="DL") rma(smd, sei=se, mods = ~ tailor, data=dat, method="DL") rma(smd, sei=se, mods = ~ pperf, data=dat, method="DL") rma(smd, sei=se, mods = ~ I(meetings * hours), data=dat, method="DL") } } \keyword{datasets} \concept{medicine} \concept{primary care} \concept{standardized mean differences} \concept{publication bias} \concept{meta-regression} \section{Concepts}{ medicine, primary care, standardized mean differences, publication bias, meta-regression } metadat/man/dat.assink2016.Rd0000644000176200001440000001140614750466700015316 0ustar liggesusers\name{dat.assink2016} \docType{data} \alias{dat.assink2016} \title{Studies on the Association between Recidivism and Mental Health} \description{Results from 17 studies on the association between recidivism and mental health in delinquent juveniles.} \usage{ dat.assink2016 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study id number \cr \bold{esid} \tab \code{numeric} \tab effect size within study id number \cr \bold{id} \tab \code{numeric} \tab row id number \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{pubstatus} \tab \code{numeric} \tab published study (0 = no; 1 = yes) \cr \bold{year} \tab \code{numeric} \tab publication year of the study (approximately mean centered) \cr \bold{deltype} \tab \code{character} \tab type of delinquent behavior in which juveniles could have recidivated (either general, overt, or covert) } } \details{ The studies included in this dataset (which is a subset of the data used in Assink et al., 2015) compared the difference in recidivism between delinquent juveniles with a mental health disorder and a comparison group of juveniles without a mental health disorder. Since studies differed in the way recidivism was defined and assessed, results are given in terms of standardized mean differences, with positive values indicating a higher prevalence of recidivism in the group of juveniles with a mental health disorder. Multiple effect size estimates could be extracted from most studies (e.g., for different delinquent behaviors in which juveniles could have recidivated), necessitating the use of appropriate models/methods for the analysis. Assink and Wibbelink (2016) illustrate the use of multilevel meta-analysis models for this purpose. } \note{ The \code{year} variable is not constant within study 3, as this study refers to two different publications using the same data. } \source{ Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. \emph{The Quantitative Methods for Psychology}, \bold{12}(3), 154--174. \verb{https://doi.org/10.20982/tqmp.12.3.p154} } \references{ Assink, M., van der Put, C. E., Hoeve, M., de Vries, S. L. A., Stams, G. J. J. M., & Oort, F. J. (2015). Risk factors for persistent delinquent behavior among juveniles: A meta-analytic review. \emph{Clinical Psychology Review}, \bold{42}, 47--61. \verb{https://doi.org/10.1016/j.cpr.2015.08.002} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.assink2016 head(dat, 9) \dontrun{ ### load metafor package library(metafor) ### fit multilevel model res <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat) res ### use cluster-robust inference methods robust(res, cluster=study, clubSandwich=TRUE) ### LRTs for the variance components res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(0,NA)) anova(res0, res) res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(NA,0)) anova(res0, res) ### examine some potential moderators via meta-regression rma.mv(yi, vi, mods = ~ pubstatus, random = ~ 1 | study/esid, data=dat) rma.mv(yi, vi, mods = ~ year, random = ~ 1 | study/esid, data=dat) dat$deltype <- relevel(factor(dat$deltype), ref="general") rma.mv(yi, vi, mods = ~ deltype, random = ~ 1 | study/esid, data=dat) rma.mv(yi, vi, mods = ~ year + deltype, random = ~ 1 | study/esid, data=dat) ### assume that the effect sizes within studies are correlated with rho=0.6 V <- vcalc(vi, cluster=study, obs=esid, data=dat, rho=0.6) round(V[dat$study \%in\% c(1,2), dat$study \%in\% c(1,2)], 4) ### fit multilevel model using this approximate V matrix res <- rma.mv(yi, V, random = ~ 1 | study/esid, data=dat) res ### use cluster-robust inference methods robust(res, cluster=study, clubSandwich=TRUE) ### use a correlation of 0.7 for effect sizes corresponding to the same type of ### delinquent behavior and a correlation of 0.5 for effect sizes corresponding ### to different types of delinquent behavior V <- vcalc(vi, cluster=study, type=deltype, obs=esid, data=dat, rho=c(0.7, 0.5)) ### fit multilevel model using this approximate V matrix res <- rma.mv(yi, V, random = ~ 1 | study/esid, data=dat) res ### use cluster-robust inference methods robust(res, cluster=study, clubSandwich=TRUE) } } \keyword{datasets} \concept{psychology} \concept{criminology} \concept{standardized mean differences} \concept{multilevel models} \concept{cluster-robust inference} \section{Concepts}{ psychology, criminology, standardized mean differences, multilevel models, cluster-robust inference } metadat/man/dat.curtin2002.Rd0000644000176200001440000000415714750466700015332 0ustar liggesusers\name{dat.curtin2002} \docType{data} \alias{dat.curtin2002} \title{Studies on Potassium Supplementation to Reduce Diastolic Blood Pressure} \description{Results from 21 cross-over studies evaluating the effect of potassium supplementation to reduce diastolic blood pressure.} \usage{ dat.curtin2002 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab first author \cr \bold{year} \tab \code{character} \tab year of publication \cr \bold{N} \tab \code{integer} \tab total sample size \cr \bold{mean} \tab \code{numeric} \tab mean difference in diastolic blood pressure \cr \bold{SE} \tab \code{numeric} \tab standard error \cr \bold{corr} \tab \code{numeric} \tab within-patient correlation \cr } } \details{ Results from 21 cross-over studies evaluating the effect of potassium supplementation to reduce diastolic blood pressure (Curtin et al., 2002, Table II). This data set is used as an example in Schwarzer et al. (2015), Chapter 2. } \source{ Curtin, F., Altman, D. G., & Elbourne, D. (2002). Meta-analysis combining parallel and cross-over clinical trials. I: Continuous outcomes. \emph{Statistics in Medicine}, \bold{21}(15), 2131--2144. \verb{https://doi.org/10.1002/sim.1205} } \references{ Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). \emph{Meta-analysis with R}. Cham, Switzerland: Springer. } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first five studies head(dat.curtin2002, 5) \dontrun{ ### Load meta package suppressPackageStartupMessages(library("meta")) ### Use DerSimonian-Laird estimator (which was the default in meta in the year 2015). ### Furthermore, print meta-analysis results with two digits. oldset <- settings.meta(method.tau = "DL", digits = 2) ### Conduct meta-analysis mg2 <- metagen(mean, SE, studlab = paste(author, year), data = dat.curtin2002, sm = "MD") mg2 ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{raw mean differences} \section{Concepts}{ raw mean differences } metadat/man/dat.gurusamy2011.Rd0000644000176200001440000001102014750466700015665 0ustar liggesusers\name{dat.gurusamy2011} \docType{data} \alias{dat.gurusamy2011} \title{Studies on Interventions to Reduce Mortality after Liver Transplantation} \description{Results from 14 trials examining the mortality risk of interventions for decreasing blood loss and blood transfusion requirements during liver transplantation.} \usage{ dat.gurusamy2011 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study information \cr \bold{treatment} \tab \code{character} \tab treatment \cr \bold{death} \tab \code{integer} \tab mortality at 60 days post-transplantation \cr \bold{n} \tab \code{integer} \tab number of individuals } } \details{ This network meta-analysis compared the effectiveness of seven interventions for decreasing blood loss and blood transfusion requirements during liver transplantation (Gurusamy et al., 2011). Fourteen studies reported mortality at 60 days, in 1,002 patients. Forty-five deaths were reported across all studies (4.5\%). Six studies observed deaths in all treatment arms while three studies did not observe any deaths. This dataset was used in Efthimiou et al. (2019) to introduce the Mantel-Haenszel method for network meta-analysis. One of the treatments (solvent detergent plasma) was only included in one study with zero events in both treatment arms; this study was excluded from all network meta-analyses. In addition, no death was observed in the antithrombin III arm of the only study evaluating this treatment which was excluded from the Mantel-Haenszel network meta-analysis. } \source{ Gurusamy, K. S., Pissanou, T., Pikhart, H., Vaughan, J., Burroughs, A. K., & Davidson, B. R. (2011). Methods to decrease blood loss and transfusion requirements for liver transplantation. \emph{Cochrane Database of Systematic Reviews}, \bold{12}, CD009052. \verb{https://doi.org/10.1002/14651858.CD009052.pub2} } \references{ Efthimiou, O., Rücker, G., Schwarzer, G., Higgins, J., Egger, M., & Salanti, G. (2019). A Mantel-Haenszel model for network meta-analysis of rare events. \emph{Statistics in Medicine}, \bold{38}(16), 2992--3012. \verb{https://doi.org/10.1002/sim.8158} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first 6 rows of the dataset head(dat.gurusamy2011) ### Only study evaluating solvent detergent plasma subset(dat.gurusamy2011, study == "Williamson 1999") ### Only study evaluating antithrombin III subset(dat.gurusamy2011, study == "Baudo 1992") \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Change appearance of confidence intervals cilayout("(", "-") ### Transform data from long arm-based format to contrast-based ### format. Argument 'sm' has to be used for odds ratio as summary ### measure; by default the risk ratio is used in the metabin function ### called internally. pw <- pairwise(treatment, death, n, studlab = study, data = dat.gurusamy2011, sm = "OR") ### Conduct Mantel-Haenszel network meta-analysis (NMA) net.MH <- netmetabin(pw, ref = "cont") ### Conduct inverse variance (IV) network meta-analysis net.IV <- netmeta(pw, ref = "cont") ### Network graph (Mantel-Haenszel NMA) netgraph(net.MH, seq = "optimal", col = "black", plastic = FALSE, points = TRUE, pch = 21, cex.points = 3, col.points = "black", bg.points = "gray", thickness = "se.fixed", number.of.studies = TRUE) ### Full network graph (based on inverse variance method, including ### study comparing Antithrombin III with Control/Placebo) netgraph(net.IV, seq = "optimal", col = "black", plastic = FALSE, points = TRUE, pch = 21, cex.points = 3, col.points = "black", bg.points = "gray", thickness = "se.fixed", number.of.studies = TRUE) ### Compare results for Mantel-Haenszel and IV NMA forest(netbind(net.MH, net.IV, random = FALSE, name = c("MH NMA", "IV NMA"))) ### Show results for Mantel-Haenszel NMA net.MH forest(net.MH) ### League table with network estimates in lower triangle and direct ### estimates in upper triangle netleague(net.MH) ### Assess inconsistency print(netsplit(net.MH), show = "both", ci = TRUE, overall = FALSE, nchar.trts = 6) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{network meta-analysis} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, odds ratios, network meta-analysis, Mantel-Haenszel method } metadat/man/dat.stowe2010.Rd0000644000176200001440000001070214750466700015157 0ustar liggesusers\name{dat.stowe2010} \docType{data} \alias{dat.stowe2010} \title{Studies on Adjuvant Treatments to Levodopa Therapy for Parkinson disease} \description{Results from 29 trials assessing efficacy of three drug classes as adjuvant treatment to levodopa therapy in patients with Parkinson disease and motor complications.} \usage{ dat.stowe2010 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{id} \tab \code{integer} \tab study id \cr \bold{t1} \tab \code{character} \tab treatment 1 \cr \bold{y1} \tab \code{numeric} \tab treatment effect arm 1 \cr \bold{sd1} \tab \code{numeric} \tab standard deviation arm 1 \cr \bold{n1} \tab \code{integer} \tab sample size arm 1 \cr \bold{t2} \tab \code{character} \tab treatment 2 \cr \bold{y2} \tab \code{numeric} \tab treatment effect arm 2 \cr \bold{sd2} \tab \code{numeric} \tab standard deviation arm 2 \cr \bold{n2} \tab \code{integer} \tab sample size arm 2 \cr \bold{t3} \tab \code{character} \tab treatment 3 \cr \bold{y3} \tab \code{numeric} \tab treatment effect arm 3 \cr \bold{sd3} \tab \code{numeric} \tab standard deviation arm 3 \cr \bold{n3} \tab \code{integer} \tab sample size arm 3 } } \details{ This dataset contains data from a Cochrane review assessing efficacy and safety of three drug classes as adjuvant treatment to levodopa therapy in patients with Parkinson disease and motor complications (Stowe et al., 2010). The authors conducted three pairwise meta-analyses comparing dopamine agonists, catechol-O-methyl transferase inhibitors (COMTI), and monoamine oxidase type B inhibitors (MAOBI) with placebo. The primary outcome was the mean reduction of the time spent in a relatively immobile \sQuote{off} phase (mean off-time), calculated in hours per day. Relative treatment effects were expressed as mean difference. Data on this outcome were available for 5,331 patients from 28 studies comparing an active treatment with placebo and one three-arm study comparing two active treatments with placebo. } \source{ Stowe, R., Ives, N., Clarke, C. E., Deane, K., Hilten, V., Wheatley, K., Gray, R., Handley, K., & Furmston, A. (2010). Evaluation of the efficacy and safety of adjuvant treatment to levodopa therapy in Parkinson's disease patients with motor complications. \emph{Cochrane Database of Systematic Reviews}, \bold{7}, CD007166. \verb{https://doi.org/10.1002/14651858.CD007166.pub2} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show results from three studies (including three-arm study LARGO) dat.stowe2010[18:20, ] \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print mean differences with two digits and standard errors with 3 ### digits oldset <- settings.meta(digits = 2, digits.se = 3) ### Transform data from wide arm-based format to contrast-based ### format. Argument 'sm' must not be provided as the mean difference ### is the default in R function metacont() called internally. pw <- pairwise(treat = list(t1, t2, t3), n = list(n1, n2, n3), mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3), studlab = study, data = dat.stowe2010, sm = "MD") ### Show calculated mean differences (TE) for three studies selstudy <- c("COMTI(E) INT-OZ", "LARGO", "COMTI(E) Nomecomt") subset(pw, studlab \%in\% selstudy)[, c(3:7, 10, 1)] ### Conduct random effects network meta-analysis (NMA) ### with placebo as reference net <- netmeta(pw, fixed = FALSE, ref = "plac") ### Show network graph netgraph(net, number = TRUE, multiarm = TRUE, cex = 1.25, offset = 0.025, cex.number = 1, pos.number.of.studies = 0.3) ### Print NMA results net ### Forest plot with NMA results forest(net) ### Forest plot showing all network estimates of active treatments ### compared with other treatments forest(net, ref = c("C", "D", "M"), baseline = FALSE, drop = TRUE) ### Treatment ranking using P-scores netrank(net) ### Rankogram with all ranking probabilities set.seed(1909) ran <- rankogram(net) ran plot(ran) ### Treatment ranking using SUCRAs netrank(ran) ### League table showing network and direct estimates netleague(net, seq = netrank(net), ci = FALSE) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{raw mean differences} \concept{network meta-analysis} \section{Concepts}{ medicine, raw mean differences, network meta-analysis } metadat/man/dat.hackshaw1998.Rd0000644000176200001440000000661314750466700015645 0ustar liggesusers\name{dat.hackshaw1998} \docType{data} \alias{dat.hackshaw1998} \title{Studies on the Risk of Lung Cancer in Women Exposed to Environmental Tobacco Smoke} \description{Results from 37 studies on the risk of lung cancer in women exposed to environmental tobacco smoke (ETS) from their smoking spouse.} \usage{ dat.hackshaw1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{author} \tab \code{character} \tab first author of study \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{country} \tab \code{character} \tab country where study was conducted \cr \bold{design} \tab \code{character} \tab study design (either cohort or case-control) \cr \bold{cases} \tab \code{numeric} \tab number of lung cancer cases \cr \bold{or} \tab \code{numeric} \tab odds ratio \cr \bold{or.lb} \tab \code{numeric} \tab lower bound of 95\% CI for the odds ratio \cr \bold{or.ub} \tab \code{numeric} \tab upper bound of 95\% CI for the odds ratio \cr \bold{yi} \tab \code{numeric} \tab log odds ratio \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance } } \details{ The dataset includes the results from 37 studies (4 cohort, 33 case-control) examining if women (who are lifelong nonsmokers) have an elevated risk for lung cancer due to exposure to environmental tobacco smoke (ETS) from their smoking spouse. Values of the log odds ratio greater than 0 indicate an increased risk of cancer in exposed women compared to women not exposed to ETS from their spouse. Note that the log odds ratios and corresponding sampling variances were back-calculated from the reported odds ratios and confidence interval (CI) bounds (see \sQuote{Examples}). Since the reported values were rounded to some extent, this introduces some minor inaccuracies into the back-calculations. The overall estimate reported in Hackshaw et al. (1997) and Hackshaw (1998) can be fully reproduced though. } \source{ Hackshaw, A. K., Law, M. R., & Wald, N. J. (1997). The accumulated evidence on lung cancer and environmental tobacco smoke. \emph{British Medical Journal}, \bold{315}(7114), 980--988. \verb{https://doi.org/10.1136/bmj.315.7114.980} Hackshaw, A. K. (1998). Lung cancer and passive smoking. \emph{Statistical Methods in Medical Research}, \bold{7}(2), 119--136. \verb{https://doi.org/10.1177/096228029800700203} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hackshaw1998 head(dat, 10) \dontrun{ ### load metafor package library(metafor) ### random-effects model using the log odds ratios res <- rma(yi, vi, data=dat, method="DL") res ### estimated average odds ratio with CI (and prediction interval) predict(res, transf=exp, digits=2) ### illustrate how the log odds ratios and corresponding sampling variances ### can be back-calculated based on the reported odds ratios and CI bounds dat$yi <- NULL dat$vi <- NULL dat <- data.frame(dat) head(dat, 10) dat <- conv.wald(out=or, ci.lb=or.lb, ci.ub=or.ub, data=dat, transf=log) head(dat, 10) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{epidemiology} \concept{smoking} \concept{odds ratios} \section{Concepts}{ medicine, oncology, epidemiology, smoking, odds ratios } metadat/man/dat.bourassa1996.Rd0000644000176200001440000001142414750466700015665 0ustar liggesusers\name{dat.bourassa1996} \docType{data} \alias{dat.bourassa1996} \title{Studies on the Association between Handedness and Eye-Dominance} \description{Results from 47 studies on the association between handedness and eye-dominance. \loadmathjax} \usage{ dat.bourassa1996 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{sample} \tab \code{numeric} \tab sample number \cr \bold{author} \tab \code{character} \tab (first) author \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{selection} \tab \code{character} \tab selection of subjects on the basis of eyedness or handedness \cr \bold{investigator} \tab \code{character} \tab investigator (psychologist, educationalist, or other) \cr \bold{hand_assess} \tab \code{character} \tab method to assess handedness (questionnaire or performance based) \cr \bold{eye_assess} \tab \code{character} \tab method to assess eyedness (see \sQuote{Details}) \cr \bold{mage} \tab \code{numeric} \tab mean age of sample \cr \bold{lh.le} \tab \code{numeric} \tab number of left-handed left-eyed individuals \cr \bold{lh.re} \tab \code{numeric} \tab number of left-handed right-eyed individuals \cr \bold{rh.le} \tab \code{numeric} \tab number of right-handed left-eyed individuals \cr \bold{rh.re} \tab \code{numeric} \tab number of right-handed right-eyed individuals \cr \bold{sex} \tab \code{character} \tab sex of the sample (combined, male, or female) } } \details{ The 47 studies included in this meta-analysis examined the association between handedness and eye-dominance (ocular dominance or eyedness). Results are given in terms of \mjeqn{2 \times 2}{2x2} tables, indicating the number of left-handed left-eyed, left-handed right-eyed, right-handed left-eyed, and right-handed right-eyed individuals in each sample. Note that some studies included multiple (independent) samples, so that the meta-analysis included 54 samples in total. Also, for some studies, the combined data of the males and females are further broken down into the two subgroups. In some studies, there was indication that the selection of subjects was not random with respect to handedness and/or eyedness. While this should not influence the size of the association as measured with the odds ratio, this invalidates those studies for assessing the overall percentage of left-eyed and left-handed individuals. Handedness was assessed in the individual studies either based on a questionnaire or based on task performance. Eyedness was assessed based on various methods: \code{E.1} methods are based on task performance, while \code{E.2.a} denotes assessment based on a questionnaire. The performance based methods could be further broken down into: \code{E.1.a.i} (monocular procedure with object/instrument held in one hand), \code{E.1.a.ii} (monocular procedure with object/instrument held in both hands), \code{E.1.b} (binocular procedure), \code{E.1.c} (a combination of the previous methods), and \code{E.1.d} (some other method). } \source{ Bourassa, D. C., McManus, I. C., & Bryden, M. P. (1996). Handedness and eye-dominance: A meta-analysis of their relationship. \emph{Laterality}, \bold{1}(1), 5--34. \verb{https://doi.org/10.1080/713754206} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bourassa1996 head(dat, 10) \dontrun{ ### load metafor package library(metafor) ### calculate log(OR) and corresponding sampling variance with 1/2 correction dat <- escalc(measure="OR", ai=lh.le, bi=lh.re, ci=rh.le, di=rh.re, data=dat, add=1/2, to="all") head(dat, 10) ### overall association between handedness and eyedness res <- rma(yi, vi, data=dat, subset=sex=="combined") res predict(res, transf=exp, digits=2) ### multilevel model to account for heterogeneity at the study and sample levels res <- rma.mv(yi, vi, random = ~ 1 | study/sample, data=dat, subset=sex=="combined") res predict(res, transf=exp, digits=2) ### restructure the dataset to keep only the male/female data when it is reported ### separately and the combined data when this is the only data reported dat <- lapply(split(dat, dat$sample), function(x) { if (nrow(x) == 3L) { x[-which(x$sex == "combined"),] } else { x } }) dat <- do.call(rbind, dat) rownames(dat) <- NULL dat ### multilevel model to account for heterogeneity at the study, sample, and subgroup levels res <- rma.mv(yi, vi, random = ~ 1 | study/sample/sex, data=dat) res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{psychology} \concept{odds ratios} \concept{multilevel models} \section{Concepts}{ psychology, odds ratios, multilevel models } metadat/man/dat.yusuf1985.Rd0000644000176200001440000000545314750466700015224 0ustar liggesusers\name{dat.yusuf1985} \docType{data} \alias{dat.yusuf1985} \title{Studies of Beta Blockers During and After Myocardial Infarction} \description{Results from studies examining the effectiveness of beta blockers for reducing mortality and reinfarction. \loadmathjax} \usage{ dat.yusuf1985 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{table} \tab \code{character} \tab table number \cr \bold{id} \tab \code{character} \tab trial id number \cr \bold{trial} \tab \code{character} \tab trial name or first author \cr \bold{ai} \tab \code{numeric} \tab number of deaths/reinfarctions in treatment group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in treatment group \cr \bold{ci} \tab \code{numeric} \tab number of deaths/reinfarctions in control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in control group } } \details{ The dataset contains table 6 (total mortality from short-term trials of oral beta blockers), 9 (total mortality at one week from trials with an initial IV dose of a beta blocker), 10 (total mortality from long-term trials with treatment starting late and mortality from day 8 onwards in long-term trials that began early and continued after discharge), 11 (nonfatal reinfarction from long-term trials of beta blockers), 12a (sudden death in long-term beta blocker trials), and 12b (nonsudden death in long-term beta blocker trials) from the meta-analysis by Yusuf et al. (1985) on the effectiveness of of beta blockers for reducing mortality and reinfarction. The article also describes what is sometimes called Peto's one-step method for meta-analyzing \mjeqn{2 \times 2}{2x2} table data. This method is implemented in the \code{\link[metafor]{rma.peto}} function. } \source{ Yusuf, S., Peto, R., Lewis, J., Collins, R., & Sleight, P. (1985). Beta blockade during and after myocardial infarction: An overview of the randomized trials. \emph{Progress in Cardiovascular Disease}, \bold{27}(5), 335--371. \verb{https://doi.org/10.1016/s0033-0620(85)80003-7} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' dat <- dat.yusuf1985 dat[dat$table == 6,] \dontrun{ ### load metafor package library(metafor) ### to select a table for the analysis tab <- "6" # either: 6, 9, 10, 11, 12a, 12b ### to double-check total counts as reported in article apply(dat[dat$table==tab,4:7], 2, sum, na.rm=TRUE) ### meta-analysis using Peto's one-step method res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(table==tab)) res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{odds ratios} \concept{Peto's method} \section{Concepts}{ medicine, cardiology, odds ratios, Peto's method } metadat/man/dat.debruin2009.Rd0000644000176200001440000000725614750466700015470 0ustar liggesusers\name{dat.debruin2009} \docType{data} \alias{dat.debruin2009} \title{Studies on Standard Care Quality and HAART-Adherence} \description{Results from 13 trials providing information about standard care quality and HAART-adherence in control groups.} \usage{ dat.debruin2009 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab (first) author of study \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{scq} \tab \code{numeric} \tab standard care quality \cr \bold{ni} \tab \code{numeric} \tab number of patients in the standard care group \cr \bold{xi} \tab \code{numeric} \tab number of patients with an undetectable viral load in standard care group \cr \bold{mi} \tab \code{numeric} \tab number of patients with a detectable viral load in standard care group \cr \bold{ethnicity} \tab \code{character} \tab dominant ethnicity of the patients in the standard care group \cr \bold{patients} \tab \code{character} \tab inclusion of patients continuing or starting (a new) treatment \cr \bold{select} \tab \code{character} \tab baseline selection of patients with adherence problems or no selection \cr \bold{sens} \tab \code{character} \tab sensitivity of viral load assessments (<400 vs. >=400 copies/ml) } } \details{ Highly active antiretroviral therapy (HAART) refers to a combination of multiple antiretroviral drugs that can effectively suppress the HIV virus. However, achieving viral suppression (to the point that the virus becomes essentially undetectable in a blood sample) requires high levels of adherence to an often complicated medication regimen. A number of trials have examined various interventions that aim to increase adherence levels. In each trial, patients receiving the intervention are compared to patients in a control group receiving standard care (often referred to as \sQuote{care as usual}). However, the quality of standard care can vary substantially between these studies. de Bruin et al. (2009) assessed the quality of standard care provided (based on a quantification of the number of behavior change techniques applied) and examined to what extent the quality of standard care was related to the proportion of patients achieving effective viral suppression in the control groups. } \source{ de Bruin, M., Viechtbauer, W., Hospers, H. J., Schaalma, H. P., & Kok, G. (2009). Standard care quality determines treatment outcomes in control groups of HAART-adherence intervention studies: Implications for the interpretation and comparison of intervention effects. \emph{Health Psychology}, \bold{28}(6), 668--674. \verb{https://doi.org/10.1037/a0015989} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.debruin2009 dat \dontrun{ ### load metafor package library(metafor) ### calculate proportions and corresponding sampling variances dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat) dat ### random-effects model res <- rma(yi, vi, data=dat) print(res, digits=2) ### mixed-effects meta-regression model with all predictors/covariates res <- rma(yi, vi, mods = ~ scq + ethnicity + patients + select + sens, data=dat) print(res, digits=3) ### mixed-effects meta-regression model with scq and ethnicity as predictors/covariates res <- rma(yi, vi, mods = ~ scq + ethnicity, data=dat) print(res, digits=3) } } \keyword{datasets} \concept{psychology} \concept{medicine} \concept{proportions} \concept{single-arm studies} \concept{meta-regression} \section{Concepts}{ psychology, medicine, proportions, single-arm studies, meta-regression } metadat/man/dat.kalaian1996.Rd0000644000176200001440000001203614750466700015446 0ustar liggesusers\name{dat.kalaian1996} \docType{data} \alias{dat.kalaian1996} \title{Studies on the Effectiveness of Coaching for the SAT} \description{Results from studies examining the effectiveness of coaching on the performance on the Scholastic Aptitude Test (SAT).} \usage{ dat.kalaian1996 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab row (effect) id \cr \bold{study} \tab \code{character} \tab study identifier \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{n1i} \tab \code{numeric} \tab number of participants in the coached group \cr \bold{n2i} \tab \code{numeric} \tab number of participants in the uncoached group \cr \bold{outcome} \tab \code{character} \tab subtest (verbal or math) \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{hrs} \tab \code{numeric} \tab hours of coaching \cr \bold{ets} \tab \code{numeric} \tab study conducted by the Educational Testing Service (ETS) (0 = no, 1 = yes) \cr \bold{homework} \tab \code{numeric} \tab assignment of homework outside of the coaching course (0 = no, 1 = yes) \cr \bold{type} \tab \code{numeric} \tab study type (1 = randomized study, 2 = matched study, 3 = nonequivalent comparison study) } } \details{ The effectiveness of coaching for the Scholastic Aptitude Test (SAT) has been examined in numerous studies. This dataset contains standardized mean differences comparing the performance of a coached versus uncoached group on the verbal and/or math subtest of the SAT. Studies may report a standardized mean difference for the verbal subtest, the math subtest, or both. In the latter case, the two standardized mean differences are not independent (since they were measured in the same group of subjects). The number of hours of coaching (variable \code{hrs}), whether the study was conducted by the Educational Testing Service (variable \code{ets}), whether homework was assigned outside of the coaching course (variable \code{homework}), and the study type (variable \code{type}) may be potential moderators of the treatment effect. } \note{ The dataset was obtained from Table 1 in Kalaian and Raudenbush (1996). However, there appear to be some inconsistencies between the data in the table and those that were actually used for the analyses (see \sQuote{Examples}). } \source{ Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. \emph{Psychological Methods}, \bold{1}(3), 227--235. \verb{https://doi.org/10.1037/1082-989X.1.3.227} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.kalaian1996 head(dat, 12) \dontrun{ ### load metafor package library(metafor) ### check ranges range(dat$yi[dat$outcome == "verbal"]) # -0.35 to 0.74 according to page 230 range(dat$yi[dat$outcome == "math"]) # -0.53 to 0.60 according to page 231 ### comparing this with Figure 1 in the paper reveals some discrepancies par(mfrow=c(1,2), mar=c(5,5,1,3.4)) plot(log(dat$hrs[dat$outcome == "verbal"]), dat$yi[dat$outcome == "verbal"], pch=19, col=rgb(0,0,0,0.4), xlab="Log(Coaching Hours)", ylab="Effect Size (verbal)", xlim=c(1,6), ylim=c(-0.5,1), xaxs="i", yaxs="i") abline(h=c(-0.5,0,0.5), lty="dotted") abline(v=log(c(5,18)), lty="dotted") plot(log(dat$hrs[dat$outcome == "math"]), dat$yi[dat$outcome == "math"], pch=19, col=rgb(0,0,0,0.4), xlab="Log(Coaching Hours)", ylab="Effect Size (math)", xlim=c(1,6), ylim=c(-1.0,1), xaxs="i", yaxs="i") abline(h=c(-0.5,0,0.5), lty="dotted") abline(v=log(c(5,18)), lty="dotted") ### construct variance-covariance matrix assuming rho = 0.66 for effect sizes ### corresponding to the 'verbal' and 'math' outcome types V <- vcalc(vi, cluster=study, type=outcome, data=dat, rho=0.66) ### fit multivariate random-effects model res <- rma.mv(yi, V, mods = ~ 0 + outcome, random = ~ outcome | study, struct="UN", data=dat, digits=3) res ### test whether the effect differs for the math and verbal subtest anova(res, X=c(1,-1)) ### log-transform and mean center the hours of coaching variable dat$loghrs <- log(dat$hrs) - mean(log(dat$hrs), na.rm=TRUE) ### fit multivariate model with log(hrs) as moderator res <- rma.mv(yi, V, mods = ~ 0 + outcome + outcome:loghrs, random = ~ outcome | study, struct="UN", data=dat, digits=3) res ### fit model with tau2 = 0 for outcome verbal (which also constrains rho = 0) res <- rma.mv(yi, V, mods = ~ 0 + outcome + outcome:loghrs, random = ~ outcome | study, struct="UN", tau2=c(NA,0), data=dat, digits=3) res } } \keyword{datasets} \concept{education} \concept{standardized mean differences} \concept{multivariate models} \concept{meta-regression} \section{Concepts}{ education, standardized mean differences, multivariate models, meta-regression } metadat/man/dat.lau1992.Rd0000644000176200001440000000521014750466700014617 0ustar liggesusers\name{dat.lau1992} \docType{data} \alias{dat.lau1992} \title{Studies on Intravenous Streptokinase for Acute Myocardial Infarction} \description{Results from 33 trials comparing intravenous streptokinase versus placebo or no therapy in patients who had been hospitalized for acute myocardial infarction.} \usage{ dat.lau1992 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{character} \tab trial name \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ai} \tab \code{numeric} \tab number of deaths in the streptokinase group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the streptokinase group \cr \bold{ci} \tab \code{numeric} \tab number of deaths in the control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the control group } } \details{ In the paper by Lau et al. (1992), the data are used to illustrate the idea of a cumulative meta-analysis, where the results are updated as each trial is added to the dataset. See \sQuote{Examples} for code that replicates the results and shows corresponding forest plots. } \source{ Lau, J., Antman, E. M., Jimenez-Silva, J., Kupelnick, B., Mosteller, F., & Chalmers, T. C. (1992). Cumulative meta-analysis of therapeutic trials for myocardial infarction. \emph{New England Journal of Medicine}, \bold{327}(4), 248--254. \verb{https://doi.org/10.1056/NEJM199207233270406} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.lau1992 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of log odds ratios using the MH method res <- rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, slab=trial) print(res, digits=2) ### forest plot forest(res, xlim=c(-11,9), atransf=exp, at=log(c(0.01, 0.1, 1, 10, 100)), ilab=dat$year, ilab.xpos=-7) text(-7, 35, "Year", font=2) ### cumulative meta-analysis sav <- cumul(res) ### forest plot of the cumulative results forest(sav, xlim=c(-5,4), atransf=exp, at=log(c(0.1, 0.5, 1, 2, 10)), ilab=dat$year, ilab.xpos=-3) text(-3, 35, "Year", font=2) id <- c(4, 8, 15, 33) # rows for which the z/p-values should be shown (as in Lau et al., 1992) text(1.1, (res$k:1)[id], paste0("z = ", fmtx(sav$zval[id], digits=2), fmtp(sav$pval[id], pname=", p", equal=TRUE, sep=TRUE, add0=TRUE))) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{odds ratios} \concept{cumulative meta-analysis} \section{Concepts}{ medicine, cardiology, odds ratios, cumulative meta-analysis } metadat/man/dat.dong2013.Rd0000644000176200001440000000677514750466700014767 0ustar liggesusers\name{dat.dong2013} \docType{data} \alias{dat.dong2013} \title{Studies on Safety of Inhaled Medications for Chronic Obstructive Pulmonary Disease} \description{Results from 41 trials examining the safety of inhaled medications in patients with chronic obstructive pulmonary disease.} \usage{ dat.dong2013 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{integer} \tab study ID \cr \bold{treatment} \tab \code{character} \tab treatment \cr \bold{death} \tab \code{integer} \tab mortality \cr \bold{randomized} \tab \code{integer} \tab number of individuals } } \details{ This network meta-analysis compared the safety of inhaled medications in patients with chronic obstructive pulmonary disease (Dong et al., 2013). Mortality was reported in 41 randomized trials, with a total of 52 462 patients. Mortality was low, with 2 408 deaths (4.6\%) reported across all studies. There were nine studies that reported zero events in at least one of the treatment arms and three additional studies had zero events in all treatment arms. This dataset was used in Efthimiou et al. (2019) to illustrate the Mantel-Haenszel method for network meta-analysis. } \source{ Dong, Y.-H., Lin, H.-H., Shau, W.-Y., Wu, Y.-C., Chang, C.-H., & Lai, M.-S. (2013). Comparative safety of inhaled medications in patients with chronic obstructive pulmonary disease: Systematic review and mixed treatment comparison meta-analysis of randomised controlled trials. \emph{Thorax}, \bold{68}(1), 48--56. \verb{https://doi.org/10.1136/thoraxjnl-2012-201926} } \references{ Efthimiou, O., Rücker, G., Schwarzer, G., Higgins, J., Egger, M., & Salanti, G. (2019). A Mantel-Haenszel model for network meta-analysis of rare events. \emph{Statistics in Medicine}, \bold{38}(16), 2992--3012. \verb{https://doi.org/10.1002/sim.8158} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first 6 rows / 3 studies of the dataset head(dat.dong2013) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Change appearance of confidence intervals cilayout("(", "-") ### Transform data from long arm-based format to contrast-based ### format. Argument 'sm' has to be used for odds ratio as summary ### measure; by default the risk ratio is used in the metabin function ### called internally. pw <- pairwise(treatment, death, randomized, studlab = id, data = dat.dong2013, sm = "OR") ### Calculated log odds ratios (TE) and standard errors (seTE) pw[1:3, 1:9] ### Conduct Mantel-Haenszel network meta-analysis (NMA) net <- netmetabin(pw, ref = "plac") ### Network graph netgraph(net, seq = "optimal", col = "black", plastic = FALSE, points = TRUE, pch = 21, cex.points = 3, col.points = "black", bg.points = "gray", thickness = "se.fixed", number.of.studies = TRUE) ### Show results for Mantel-Haenszel NMA net forest(net) ### League table with network estimates in lower triangle and direct ### estimates in upper triangle netleague(net) ### Assess inconsistency print(netsplit(net), show = "both", ci = TRUE, overall = FALSE, nchar.trts = 6) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{network meta-analysis} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, odds ratios, network meta-analysis, Mantel-Haenszel method } metadat/man/metadat-package.Rd0000644000176200001440000001164714750466700015765 0ustar liggesusers\name{metadat-package} \alias{metadat-package} \alias{metadat} \docType{package} \title{Meta-Analysis Datasets for R} \description{ The \pkg{metadat} package contains a large collection of meta-analysis datasets. These datasets are useful for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. } \section{Browsing and Searching for Datasets}{ A listing of all datasets in the package can be obtained with \code{help(package=metadat)}. Each datasets is also tagged with one or multiple concept terms. These concept terms refer to various aspects of a dataset, such as the field/topic of research, the outcome measure used for the analysis, the model(s) used for analyzing the data, and the methods/concepts that can be illustrated with the dataset. The \code{\link{datsearch}} function can be used to search among the existing datasets in the package based on their concept terms or based on a full-text search of their corresponding help files. You can also read the documentation online at \url{https://wviechtb.github.io/metadat/} (where the output from the example analyses corresponding to each dataset is provided). } \section{Contributing New Datasets}{ We welcome contributions of new datasets to the package. For each dataset, there must be a citable reference, ideally in a peer-reviewed journal or publication. The general workflow for contributing a new dataset is as follows: \itemize{ \item Install the \code{metadat} package in R in the usual manner (i.e., \code{install.packages("metadat")}). \item If you are familiar with Git/GitHub and making pull requests, fork the \href{https://github.com/wviechtb/metadat}{package repository}. Otherwise, \href{https://github.com/wviechtb/metadat/archive/master.zip}{download} the source version of the package from GitHub and unzip the file to some directory on your computer. \item Place the raw data (in a non-binary format) in the \code{data-raw} directory. The file should be named \code{dat..}, where \code{} is the last name of the first author of the publication from which the data come, \code{} is the publication year, and \code{} is the file extension (e.g., \code{.txt}, \code{.csv}). \item Place a corresponding R script in the \code{data-raw} directory named \code{dat..r} that reads in the data, possibly does some data cleaning/processing, and then saves the dataset to the \code{data} directory (using \code{\link{save}}), with name \code{dat..rda}. \item Start R, load the \code{metadat} package (i.e., \code{library(metadat)}), and then run the \code{\link{prep_dat}} function (either set the working directory to the location of the source package beforehand or use the \code{pkgdir} argument of the \code{\link{prep_dat}} function to specify the source package location). \item For a new dataset, this should create a boilerplate template for a corresponding help file in the \code{man} directory, named \code{dat..Rd}. Edit the help file, adding the title and a short description of the dataset in general, a description of each variable in the dataset, further details on the dataset (e.g., the field of research, how the data was collected, the purpose of the dataset / what it was used for, the effect size or outcome measure used in the analysis, the types of analyses/models that can be illustrated with the dataset), a reference for the source of the dataset, one or multiple concept terms, the name and email address of the contributor of the dataset, and (optionally) example code to illustrate the analysis of the dataset. \item Either make a pull request (if you are familiar with this workflow) or zip up the \code{dat..}, \code{dat..r}, \code{dat..rda}, and \code{dat..Rd} files and open up a new \href{https://github.com/wviechtb/metadat/issues}{issue at GitHub}, attaching the zip file. \item If the above makes no sense to you, you can also email one of the package authors with a cleaned, raw data file in \code{.txt} or \code{.csv} format, along with a meta-data file (format doesn't matter) that includes the information described above. } } \section{Citing the Package}{ If you use these data, please cite both the \pkg{metadat} package (see \code{citation("metadat")} for the reference) and the original source of the data as given under the help file of a dataset. } \section{Bug/Error Reports}{ If you think you have found an error in an existing dataset or a bug in the package in general, please go to \url{https://github.com/wviechtb/metadat/issues} and open up a new issue. } \author{ Thomas White, \email{thomas.white@sydney.edu.au} \cr Daniel Noble, \email{daniel.noble@anu.edu.au} \cr Alistair Senior, \email{alistair.senior@sydney.edu.au} \cr W. Kyle Hamilton, \email{whamilton@ucmerced.edu} \cr Wolfgang Viechtbauer, \email{wvb@metafor-project.org} } \keyword{package} metadat/man/dat.senn2013.Rd0000644000176200001440000002333414750466700014771 0ustar liggesusers\name{dat.senn2013} \docType{data} \alias{dat.senn2013} \title{Studies on the Effectiveness of Glucose-Lowering Agents} \description{Results from 26 trials examining the effectiveness of glucose-lowering agents in patients with type 2 diabetes} \usage{ dat.senn2013 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab (first) author and year of study \cr \bold{ni} \tab \code{numeric} \tab sample size of the study arm \cr \bold{treatment} \tab \code{character} \tab treatment given \cr \bold{comment} \tab \code{character} \tab whether figures given are based on raw values at outcome or on change from baseline \cr \bold{mi} \tab \code{numeric} \tab raw mean or mean change \cr \bold{sdi} \tab \code{numeric} \tab standard deviation } } \details{ The dataset includes the results from 26 randomized controlled trials examining the effectiveness of adding various oral glucose-lowering agents to a baseline sulfonylurea therapy in patients with type 2 diabetes. The outcome measured in the studies was either the mean HbA1c level at follow-up or the mean change in HbA1c level from baseline to follow-up. A total of 10 different treatment types were examined in these studies: acarbose, benfluorex, metformin, miglitol, pioglitazone, placebo, rosiglitazone, sitagliptin, sulfonylurea alone, and vildagliptin. One study included three treatment arms (Willms, 1999), while the rest of the studies included two treatment arms (hence, the dataset includes the results from 53 treatment arms). The data can be used for a network meta-analysis, either using an arm-based or a contrast-based model. See \sQuote{Examples} below. } \source{ Senn, S., Gavini, F., Magrez, D., & Scheen, A. (2013). Issues in performing a network meta-analysis. \emph{Statistical Methods in Medical Research}, \bold{22}(2), 169--189. \verb{https://doi.org/10.1177/0962280211432220} } \references{ Law, M., Jackson, D., Turner, R., Rhodes, K., & Viechtbauer, W. (2016). Two new methods to fit models for network meta-analysis with random inconsistency effects. \emph{BMC Medical Research Methodology}, \bold{16}, 87. \verb{https://doi.org/10.1186/s12874-016-0184-5} Rücker, G., & Schwarzer, G. (2015). Ranking treatments in frequentist network meta-analysis works without resampling methods. \emph{BMC Medical Research Methodology}, \bold{15}, 58. \verb{https://doi.org/10.1186/s12874-015-0060-8} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.senn2013 dat \dontrun{ ### load metafor package library(metafor) ### create network graph ('igraph' package must be installed) library(igraph, warn.conflicts=FALSE) pairs <- data.frame(do.call(rbind, sapply(split(dat$treatment, dat$study), function(x) t(combn(x,2)))), stringsAsFactors=FALSE) pairs$X1 <- factor(pairs$X1, levels=sort(unique(dat$treatment))) pairs$X2 <- factor(pairs$X2, levels=sort(unique(dat$treatment))) tab <- table(pairs[,1], pairs[,2]) tab # adjacency matrix g <- graph_from_adjacency_matrix(tab, mode = "plus", weighted=TRUE, diag=FALSE) plot(g, edge.curved=FALSE, edge.width=E(g)$weight, layout=layout_as_star(g, center="placebo"), vertex.size=45, vertex.color="lightgray", vertex.label.color="black", vertex.label.font=2) ### table of studies versus treatments examined print(addmargins(table(dat$study, dat$treatment)), zero.print="") ### table of frequencies with which treatment pairs were studied print(as.table(crossprod(table(dat$study, dat$treatment))), zero.print="") ### add means and sampling variances of the means to the dataset dat <- escalc(measure="MN", mi=mi, sdi=sdi, ni=ni, data=dat) ### turn treatment variable into factor and set reference level dat$treatment <- relevel(factor(dat$treatment), ref="placebo") ### add a space before each level (this makes the output a bit more legible) levels(dat$treatment) <- paste0(" ", levels(dat$treatment)) ### network meta-analysis using an arm-based fixed-effects model with fixed study effects res.fe <- rma.mv(yi, vi, mods = ~ 0 + study + treatment, data=dat, slab=paste0(study, treatment)) res.fe ### test if treatment factor as a whole is significant anova(res.fe, btt="treatment") ### forest plot of the contrast estimates (treatments versus placebos) forest(tail(coef(res.fe), 9), tail(diag(vcov(res.fe)), 9), slab=levels(dat$treatment)[-1], xlim=c(-2.5, 1.5), alim=c(-1.5, 0.5), psize=1, xlab="Estimate", header="Treatment") ### weight matrix for the estimation of the fixed effects (leaving out the study effects) w <- t(tail(vcov(res.fe) \%*\% t(model.matrix(res.fe)) \%*\% weights(res.fe, type="matrix"), 9)) rownames(w) <- res.fe$slab ### create shade plot for the diabetes network with placebo as the reference treatment ### negative values in blue shades, positive values in red shades cols <- colorRampPalette(c("blue", "gray95", "red"))(9) heatmap(w, Rowv=NA, Colv=NA, scale="none", margins=c(6,11), col=cols, cexRow=.7, cexCol=1, labCol=levels(dat$treatment)[-1]) ### network meta-analysis using an arm-based random-effects model with fixed study effects ### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons res.re <- rma.mv(yi, vi, mods = ~ 0 + study + treatment, random = ~ treatment | study, rho=1/2, data=dat, slab=paste0(study, treatment)) res.re ### test if treatment factor as a whole is significant anova(res.re, btt="treatment") ### forest plot of the contrast estimates (treatments versus placebos) forest(tail(coef(res.re), 9), tail(diag(vcov(res.re)), 9), slab=levels(dat$treatment)[-1], xlim=c(-2.5, 1.5), alim=c(-1.5, 0.5), psize=1, xlab="Estimate", header="Treatment") ### compute the contribution of each study to the overall Q-test value qi <- sort(by((resid(res.fe) / sqrt(dat$vi))^2, dat$study, sum)) ### check that the values add up sum(qi) res.fe$QE ### plot the values s <- length(qi) par(mar=c(5,10,2,1)) plot(qi, 1:s, pch=19, xaxt="n", yaxt="n", xlim=c(0,40), xlab="Chi-Square Contribution", ylab="") axis(side=1) axis(side=2, at=1:s, labels=names(qi), las=1, tcl=0) segments(rep(0,s), 1:s, qi, 1:s) ############################################################################ ### restructure dataset to a contrast-based format dat <- dat.senn2013[c(1,4:2,5:6)] # reorder variables first dat <- to.wide(dat, study="study", grp="treatment", ref="placebo", grpvars=4:6) dat ### calculate mean difference and corresponding sampling variance for each treatment comparison dat <- escalc(measure="MD", m1i=mi.1, sd1i=sdi.1, n1i=ni.1, m2i=mi.2, sd2i=sdi.2, n2i=ni.2, data=dat) dat ### calculate the variance-covariance matrix of the mean differences for the multitreatment studies calc.v <- function(x) { v <- matrix(x$sdi.2[1]^2 / x$ni.2[1], nrow=nrow(x), ncol=nrow(x)) diag(v) <- x$vi v } V <- bldiag(lapply(split(dat, dat$study), calc.v)) ### add contrast matrix to dataset dat <- contrmat(dat, grp1="treatment.1", grp2="treatment.2") dat ### network meta-analysis using a contrast-based random-effects model ### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons ### the treatment left out (placebo) becomes the reference level for the treatment comparisons res <- rma.mv(yi, V, mods = ~ 0 + acarbose + benfluorex + metformin + miglitol + pioglitazone + rosiglitazone + sitagliptin + sulfonylurea + vildagliptin, random = ~ comp | study, rho=1/2, data=dat) res ### forest plot of the contrast estimates (treatments versus placebos) forest(coef(res), diag(vcov(res)), slab=names(coef(res)), order="obs", xlim=c(-3.0, 2.5), alim=c(-1.5, 0.5), psize=1, xlab="Estimate", header="Treatment") ### estimate all pairwise differences between treatments contr <- data.frame(t(combn(names(coef(res)), 2))) contr <- contrmat(contr, "X1", "X2", last="vildagliptin") rownames(contr) <- paste(contr$X1, "-", contr$X2) contr <- as.matrix(contr[-c(1:2)]) sav <- predict(res, newmods=contr) sav[["slab"]] <- rownames(contr) sav ### fit random inconsistency effects model (see Law et al., 2016) inc <- rma.mv(yi, V, mods = ~ 0 + acarbose + benfluorex + metformin + miglitol + pioglitazone + rosiglitazone + sitagliptin + sulfonylurea + vildagliptin, random = list(~ comp | study, ~ comp | design), rho=1/2, phi=1/2, data=dat) inc ############################################################################ ### compute P-scores (see Rücker & Schwarzer, 2015) contr <- data.frame(t(combn(c(names(coef(res)),"placebo"), 2))) # add 'placebo' to contrast matrix contr <- contrmat(contr, "X1", "X2", last="placebo", append=FALSE) b <- c(coef(res),0) # add 0 for 'placebo' (the reference treatment) vb <- bldiag(vcov(res),0) # add 0 row/column for 'placebo' (the reference treatment) pvals <- apply(contr, 1, function(x) pnorm((x\%*\%b) / sqrt(t(x)\%*\%vb\%*\%x))) tab <- vec2mat(pvals, corr=FALSE) tab[upper.tri(tab)] <- t((1 - tab)[upper.tri(tab)]) rownames(tab) <- colnames(tab) <- colnames(contr) round(tab, 2) # like Table 2 in the article cbind(pscore=round(sort(apply(tab, 1, mean, na.rm=TRUE), decreasing=TRUE), 3)) # note: the values are slightly different from the ones given in Table 3 of Rücker and # Schwarzer (2015) since model 'res' above is fitted using REML estimation while the # results shown in the article are based on the 'netmeta' package, which uses a DL-type # estimator for the amount of heterogeneity by default ############################################################################ } } \keyword{datasets} \concept{medicine} \concept{raw mean differences} \concept{network meta-analysis} \section{Concepts}{ medicine, raw mean differences, network meta-analysis } metadat/man/dat.bornmann2007.Rd0000644000176200001440000000623514750466700015644 0ustar liggesusers\name{dat.bornmann2007} \docType{data} \alias{dat.bornmann2007} \title{Studies on Gender Differences in Grant and Fellowship Awards} \description{Results from 21 studies on gender differences in grant and fellowship awards.} \usage{ dat.bornmann2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study reference \cr \bold{obs} \tab \code{numeric} \tab observation within study \cr \bold{doctype} \tab \code{character} \tab document type \cr \bold{gender} \tab \code{character} \tab gender of the study authors \cr \bold{year} \tab \code{numeric} \tab (average) cohort year \cr \bold{org} \tab \code{character} \tab funding organization / program \cr \bold{country} \tab \code{character} \tab country of the funding organization / program \cr \bold{type} \tab \code{character} \tab fellowship or grant application \cr \bold{discipline} \tab \code{character} \tab discipline / field \cr \bold{waward} \tab \code{numeric} \tab number of women who received a grant/fellowship award \cr \bold{wtotal} \tab \code{numeric} \tab number of women who applied for an award \cr \bold{maward} \tab \code{numeric} \tab number of men who received a grant/fellowship award \cr \bold{mtotal} \tab \code{numeric} \tab number of men who applied for an award } } \details{ The studies in this dataset examine whether the chances of receiving a grant or fellowship award differs for men and women. Note that many studies provide multiple comparisons (e.g., for different years / cohorts / disciplines). A multilevel meta-analysis model can be used to account for the multilevel structure in these data. } \source{ Bornmann, L., Mutz, R., & Daniel, H. (2007). Gender differences in grant peer review: A meta-analysis. \emph{Journal of Informetrics}, \bold{1}(3), 226--238. \verb{https://doi.org/10.1016/j.joi.2007.03.001} } \references{ Marsh, H. W., Bornmann, L., Mutz, R., Daniel, H.-D., & O'Mara, A. (2009). Gender effects in the peer reviews of grant proposals: A comprehensive meta-analysis comparing traditional and multilevel approaches. \emph{Review of Educational Research}, \bold{79}(3), 1290--1326. \verb{https://doi.org/10.3102/0034654309334143} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bornmann2007 head(dat, 16) \dontrun{ ### load metafor package library(metafor) ### calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=waward, n1i=wtotal, ci=maward, n2i=mtotal, data=dat) ### fit multilevel meta-analysis model res <- rma.mv(yi, vi, random = ~ 1 | study/obs, data=dat) res ### estimated average odds ratio (with 95\% CI/PI) predict(res, transf=exp, digits=2) ### test for a difference between fellowship and grant applications res <- rma.mv(yi, vi, mods = ~ type, random = ~ 1 | study/obs, data=dat) res predict(res, newmods=0:1, transf=exp, digits=2) } } \keyword{datasets} \concept{sociology} \concept{odds ratios} \concept{multilevel models} \section{Concepts}{ sociology, odds ratios, multilevel models } metadat/man/dat.white2020.Rd0000644000176200001440000000604714750466700015146 0ustar liggesusers\name{dat.white2020} \docType{data} \alias{dat.white2020} \title{Studies on the Relationship between Sexual Signal Expression and Individual Quality} \description{Results from 41 studies examining the relationship between measures of individual quality and the expression of structurally coloured sexual signals.} \usage{ dat.white2020 } \format{ The object is a data frame which contains the following columns: \tabular{lll}{ \bold{study_id} \tab \code{character} \tab study-level ID \cr \bold{obs} \tab \code{character} \tab observation-level ID \cr \bold{exp_obs} \tab \code{character} \tab whether the study is observational or experimental \cr \bold{control} \tab \code{numeric} \tab whether the study did (1) or did not (0) include a non-sexual control trait \cr \bold{class} \tab \code{character} \tab class of the study organisms \cr \bold{genus} \tab \code{character} \tab class of the study organisms \cr \bold{species} \tab \code{character} \tab species of the study organisms \cr \bold{sex} \tab \code{character} \tab sex of the study organisms \cr \bold{iridescent} \tab \code{numeric} \tab whether the colour signals were iridescent (1) or not (0) \cr \bold{col_var} \tab \code{character} \tab the colour variable quantified \cr \bold{col_component} \tab \code{character} \tab whether the colour variable is chromatic or achromatic \cr \bold{quality_measure} \tab \code{character} \tab the measure of individual quality used \cr \bold{region} \tab \code{character} \tab the body region from which colour was sampled \cr \bold{n} \tab \code{numeric} \tab study sample size \cr \bold{r} \tab \code{numeric} \tab Pearson's correlation coefficient \cr } } \details{ The 186 rows in this dataset come from 41 experimental and observational studies reporting on the correlation between measures of individual quality (age, body condition, immune function, parasite resistance) and the expression of structurally coloured sexual signals across 28 species. The purpose of this meta-analysis was to test whether structural colour signals show heightened condition-dependent expression, as predicted by evolutionary models of 'honest' signalling. } \source{ White, T. E. (2020). Structural colours reflect individual quality: A meta-analysis. \emph{Biology Letters}, \bold{16}(4), 20200001. \verb{https://doi.org/10.1098/rsbl.2020.0001} } \author{ Thomas E. White, \email{thomas.white@sydney.edu.au} } \examples{ ### copy data into 'dat' and examine data dat <- dat.white2020 head(dat, 10) \dontrun{ ### load metafor package library(metafor) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=r, ni=n, data=dat) ### fit multilevel meta-analytic model res <- rma.mv(yi, vi, random = list(~ 1 | study_id, ~ 1 | obs), data=dat) res } } \keyword{datasets} \concept{ecology} \concept{evolution} \concept{correlation coefficients} \section{Concepts}{ ecology, evolution, correlation coefficients } metadat/man/dat.pagliaro1992.Rd0000644000176200001440000000705214750466700015642 0ustar liggesusers\name{dat.pagliaro1992} \docType{data} \alias{dat.pagliaro1992} \title{Studies on the Effectiveness of Nonsurgical Treatments in Cirrhosis} \description{Results from 26 trials examining the effectiveness of beta-blockers and sclerotherapy for the prevention of first bleeding in patients with cirrhosis} \usage{ dat.pagliaro1992 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study id \cr \bold{trt} \tab \code{character} \tab either beta-blockers, sclerotherapy, or control \cr \bold{xi} \tab \code{numeric} \tab number of patients with first bleeding \cr \bold{ni} \tab \code{numeric} \tab number of patients treated } } \details{ The dataset includes the results from 26 randomized controlled trials examining the effectiveness of nonsurgical treatments for the prevention of first bleeding in patients with cirrhosis. Patients were either treated with beta-blockers, endoscopic sclerotherapy, or with a nonactive treatment (control). Two trials included all three treatment conditions, 7 trials compared beta-blockers against control, and 17 trials compared sclerotherapy against control. The dataset has been used in various papers to illustrate methods for conducting a network meta-analysis / mixed treatment comparison. } \source{ Pagliaro, L., D'Amico, G., \enc{Sörensen}{Soerensen}, T. I. A., Lebrec, D., Burroughs, A. K., Morabito, A., \enc{Tiné}{Tine}, F., Politi, F., & Traina, M. (1992). Prevention of first bleeding in cirrhosis: A meta-analysis of randomized trials of nonsurgical treatment. \emph{Annals of Internal Medicine}, \bold{117}(1), 59--70. \verb{https://doi.org/10.7326/0003-4819-117-1-59} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.pagliaro1992 dat \dontrun{ ### load metafor package library(metafor) ### restructure dataset to a contrast-based format dat.c <- to.wide(dat, study="study", grp="trt", grpvars=3:4) dat.c ### Mantel-Haenszel results for beta-blockers and sclerotherapy versus control, respectively rma.mh(measure="OR", ai=xi.1, n1i=ni.1, ci=xi.2, n2i=ni.2, data=dat.c, subset=(trt.1=="beta-blockers"), digits=2) rma.mh(measure="OR", ai=xi.1, n1i=ni.1, ci=xi.2, n2i=ni.2, data=dat.c, subset=(trt.1=="sclerotherapy"), digits=2) ### calculate log odds for each study arm dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat) dat ### turn treatment variable into factor and set reference level dat$trt <- relevel(factor(dat$trt), ref="control") ### add a space before each level (this makes the output a bit more legible) levels(dat$trt) <- paste0(" ", levels(dat$trt)) ### network meta-analysis using an arm-based random-effects model with fixed study effects ### (by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons) res <- rma.mv(yi, vi, mods = ~ 0 + factor(study) + trt, random = ~ trt | study, rho=1/2, data=dat) res ### average odds ratio comparing beta-blockers and sclerotherapy versus control, respectively predict(res, newmods=c(rep(0,26), 1, 0), transf=exp, digits=2) predict(res, newmods=c(rep(0,26), 0, 1), transf=exp, digits=2) ### average odds ratio comparing beta-blockers versus sclerotherapy predict(res, newmods=c(rep(0,26), 1, -1), transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{Mantel-Haenszel method} \concept{network meta-analysis} \section{Concepts}{ medicine, odds ratios, Mantel-Haenszel method, network meta-analysis } metadat/man/dat.lee2004.Rd0000644000176200001440000000407514750466700014574 0ustar liggesusers\name{dat.lee2004} \docType{data} \alias{dat.lee2004} \title{Studies on Acupoint P6 Stimulation for Preventing Nausea} \description{Results from studies examining the effectiveness of wrist acupuncture point P6 stimulation for preventing postoperative nausea.} \usage{ dat.lee2004 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab trial id number \cr \bold{study} \tab \code{character} \tab first author \cr \bold{year} \tab \code{numeric} \tab study year \cr \bold{ai} \tab \code{numeric} \tab number of patients experiencing nausea in the treatment group \cr \bold{n1i} \tab \code{numeric} \tab total number of patients in treatment group \cr \bold{ci} \tab \code{numeric} \tab number of patients experiencing nausea in the sham group \cr \bold{n2i} \tab \code{numeric} \tab total number of patients in the sham group } } \details{ Postoperative nausea and vomiting are common complications following surgery and anaesthesia. As an alternative to drug therapy, acupuncture has been studied as a potential treatment in several trials. The dataset contains the results from 16 clinical trials examining the effectiveness of wrist acupuncture point P6 stimulation for preventing postoperative nausea. } \source{ Lee, A., & Done, M. L. (2004). Stimulation of the wrist acupuncture point P6 for preventing postoperative nausea and vomiting. \emph{Cochrane Database of Systematic Reviews}, \bold{3}, CD003281. \verb{https://doi.org/10.1002/14651858.CD003281.pub2} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.lee2004 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis based on log risk ratios res <- rma(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat) res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{alternative medicine} \concept{risk ratios} \section{Concepts}{ medicine, alternative medicine, risk ratios } metadat/man/dat.mcdaniel1994.Rd0000644000176200001440000001145214750466700015621 0ustar liggesusers\name{dat.mcdaniel1994} \docType{data} \alias{dat.mcdaniel1994} \title{Studies on the Validity of Employment Interviews} \description{Results from 160 studies on the correlation between employment interview assessments and job performance.} \usage{ dat.mcdaniel1994 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{ni} \tab \code{numeric} \tab sample size of the study \cr \bold{ri} \tab \code{numeric} \tab observed correlation \cr \bold{type} \tab \code{character} \tab interview type (j = job-related, s = situational, p = psychological) \cr \bold{struct} \tab \code{character} \tab interview structure (u = unstructured, s = structured) } } \details{ The 160 studies provide data in terms of the correlation between employment interview performance and actual job performance. In addition, the interview type and the interview structure are indicated. McDaniel et al. (1994) describe the interview type and structure variables as follows. "Questions in situational interviews [...] focus on the individual's ability to project what his or her behavior would be in a given situation. [...] Job-related interviews are those in which the interviewer is a personnel officer or hiring authority and the questions attempt to assess past behaviors and job-related information, but most questions are not considered situational. Psychological interviews are conducted by a psychologist, and the questions are intended to assess personal traits, such as dependability." In structured interviews, "the questions and acceptable responses were specified in advance and the responses were rated for appropriateness of content. [...] Unstructured interviews gather applicant information in a less systematic manner than do structured interviews. Although the questions may be specified in advance, they usually are not, and there is seldom a formalized scoring guide. Also, all persons being interviewed are not typically asked the same questions." The goal of the meta-analysis was to examine the overall criterion-related validity of employment interviews and to examine whether the validity depends on the type and structure of the interview. The data in this dataset were obtained from Table A.2 in Rothstein, Sutton, and Borenstein (2005, p. 325-329). Note that the \code{type} and \code{struct} variables contain some \code{NA}s. } \source{ Rothstein, H. R., Sutton, A. J., & Borenstein, M. (Eds.). (2005). \emph{Publication bias in meta-analysis: Prevention, assessment, and adjustments}. Chichester, England: Wiley. } \references{ McDaniel, M. A., Whetzel, D. L., Schmidt, F. L., & Maurer, S. D. (1994). The validity of employment interviews: A comprehensive review and meta-analysis. \emph{Journal of Applied Psychology}, \bold{79}(4), 599--616. \verb{https://doi.org/10.1037/0021-9010.79.4.599} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.mcdaniel1994 head(dat) \dontrun{ ### load metafor package library(metafor) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat) head(dat) ### meta-analysis of the transformed correlations using a random-effects model res <- rma(yi, vi, data=dat) res ### average correlation with 95\% CI predict(res, transf=transf.ztor, digits=2) ### mixed-effects model with interview type as factor ### note: job-related interviews is the reference level res <- rma(yi, vi, mods = ~ factor(type), data=dat) res ### estimated average correlation for each level of interview type res <- rma(yi, vi, mods = ~ 0 + factor(type), data=dat) predict(res, newmods=diag(3), transf=transf.ztor, digits=2) ### mixed-effects model with interview structure as factor ### note: structured interviews is the reference level res <- rma(yi, vi, mods = ~ factor(struct), data=dat) res ### estimated average correlation for each level of interview structure res <- rma(yi, vi, mods = ~ 0 + factor(struct), data=dat) predict(res, newmods=diag(2), transf=transf.ztor, digits=2) ### note: the interpretation of the results is difficult since all ### situational interviews were structured, almost all psychological ### interviews were unstructured, and actually for the majority of ### the psychological interviews it was unknown whether the interview ### was structured or unstructured table(dat$type, dat$struct, useNA="always") ### meta-analysis of raw correlations using a random-effects model res <- rma(measure="COR", ri=ri, ni=ni, data=dat.mcdaniel1994) res } } \keyword{datasets} \concept{psychology} \concept{correlation coefficients} \concept{meta-regression} \section{Concepts}{ psychology, correlation coefficients, meta-regression } metadat/man/dat.konstantopoulos2011.Rd0000644000176200001440000000751314750466700017307 0ustar liggesusers\name{dat.konstantopoulos2011} \docType{data} \alias{dat.konstantopoulos2011} \title{Studies on the Effects of Modified School Calendars on Student Achievement} \description{Results from 56 studies on the effects of modified school calendars on student achievement.} \usage{ dat.konstantopoulos2011 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{district} \tab \code{numeric} \tab district id number \cr \bold{school} \tab \code{numeric} \tab school id number (within district) \cr \bold{study} \tab \code{numeric} \tab study id number \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{year} \tab \code{numeric} \tab year of the study } } \details{ Instead of following the more traditional school calendar with a long summer break (in addition to a short winter and spring break), some schools have switched to a modified school calendar comprising more frequent but shorter intermittent breaks (e.g., 9 weeks of school followed by 3 weeks off), while keeping the total number of days at school approximately the same. The effects of using such a modified calendar on student achievement have been examined in a number of studies and were meta-analyzed by Cooper et al. (2003). The dataset (taken from Konstantopoulos, 2011) contains the results from 56 studies, each comparing the level of academic achievement in a group of students following a modified school calendar with that of a group of students following a more traditional school calendar. The difference between the two groups was quantified in terms of a standardized mean difference (with positive values indicating a higher mean level of achievement in the group following the modified school calendar). The studies were conducted at various schools that were clustered within districts. The data therefore have a multilevel structure, with schools nested within districts. A multilevel meta-analysis of these data can be used to estimate and account for the amount of heterogeneity between districts and between schools within districts. } \source{ Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. \emph{Research Synthesis Methods}, \bold{2}(1), 61--76. \verb{https://doi.org/10.1002/jrsm.35} } \references{ Cooper, H., Valentine, J. C., Charlton, K., & Melson, A. (2003). The effects of modified school calendars on student achievement and on school and community attitudes. \emph{Review of Educational Research}, \bold{73}(1), 1--52. \verb{https://doi.org/10.3102/00346543073001001} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.konstantopoulos2011 dat \dontrun{ ### load metafor package library(metafor) ### fit random-effects model res <- rma(yi, vi, data=dat) print(res, digits=3) ### fit random-effects model using rma.mv() res <- rma.mv(yi, vi, random = ~ 1 | study, data=dat) print(res, digits=3) ### fit multilevel random-effects model res.ml <- rma.mv(yi, vi, random = ~ 1 | district/school, data=dat) print(res.ml, digits=3) ### profile variance components profile(res.ml, progbar=FALSE) ### fit multivariate parameterization of the model res.mv <- rma.mv(yi, vi, random = ~ school | district, data=dat) print(res.mv, digits=3) ### tau^2 = sum of the two variance components from the multilevel model round(sum(res.ml$sigma2), digits=3) ### rho = intraclass correlation coefficient based on the multilevel model round(res.ml$sigma2[1] / sum(res.ml$sigma2), digits=3) } } \keyword{datasets} \concept{education} \concept{standardized mean differences} \concept{multilevel models} \section{Concepts}{ education, standardized mean differences, multilevel models } metadat/man/dat.moura2021.Rd0000644000176200001440000001106714750466700015150 0ustar liggesusers\name{dat.moura2021} \docType{data} \alias{dat.moura2021} \title{Studies on Assortative Mating} \description{Results from 457 studies on assortative mating in various species.} \usage{ dat.moura2021 } \format{ The object is a list containing a data frame called \code{dat} that contains the following columns and a phylogenetic tree called \code{tree}: \tabular{lll}{ \bold{study.id} \tab \code{character} \tab study id \cr \bold{effect.size.id} \tab \code{numeric} \tab effect size id \cr \bold{species} \tab \code{character} \tab species \cr \bold{species.id} \tab \code{character} \tab species id (as in the Open Tree of Life reference taxonomy) \cr \bold{subphylum} \tab \code{character} \tab the subphyla of the species \cr \bold{phylum} \tab \code{character} \tab the phyla of the species \cr \bold{assortment.trait} \tab \code{character} \tab the measure of body size \cr \bold{trait.dimensions} \tab \code{character} \tab dimensionality of the measure \cr \bold{field.collection} \tab \code{character} \tab whether data were collected in the field \cr \bold{publication.year} \tab \code{numeric} \tab publication year of the study \cr \bold{pooled.data} \tab \code{character} \tab whether data were pooled either spatially and/or temporally \cr \bold{spatially.pooled} \tab \code{character} \tab whether data were pooled spatially \cr \bold{temporally.pooled} \tab \code{character} \tab whether data were pooled temporally \cr \bold{ri} \tab \code{numeric} \tab correlation coefficient \cr \bold{ni} \tab \code{numeric} \tab sample size } } \details{ The 457 studies included in this dataset provide 1828 correlation coefficients describing the similarity in some measure of body size in mating couples in 341 different species. } \source{ Rios Moura, R., Oliveira Gonzaga, M., Silva Pinto, N., Vasconcellos-Neto, J., & Requena, G. S. (2021). Assortative mating in space and time: Patterns and biases. \emph{Ecology Letters}, \bold{24}(5), 1089--1102. \verb{https://doi.org/10.1111/ele.13690} } \references{ Cinar, O., Nakagawa, S., & Viechtbauer, W. (in press). Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. \emph{Methods in Ecology and Evolution}. \verb{https://doi.org/10.1111/2041-210X.13760} Hadfield, J. D., & Nakagawa, S. (2010). General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. \emph{Journal of Evolutionary Biology}, \bold{23}(3), 494--508. \verb{https://doi.org/10.1111/j.1420-9101.2009.01915.x} Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. \emph{Evolutionary Ecology}, \bold{26}(5), 1253--1274. \verb{https://doi.org/10.1007/s10682-012-9555-5} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.moura2021$dat head(dat) \dontrun{ ### load metafor package library(metafor) ### load ape package library(ape, warn.conflicts=FALSE) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat) ### copy tree to 'tree' tree <- dat.moura2021$tree ### turn tree into an ultrametric one tree <- compute.brlen(tree) ### compute phylogenetic correlation matrix A <- vcv(tree, corr=TRUE) ### make copy of the species.id variable dat$species.id.phy <- dat$species.id ### fit multilevel phylogenetic meta-analytic model res <- rma.mv(yi, vi, random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy), R=list(species.id.phy=A), data=dat) res ### examine if spatial and/or temporal pooling of data tends to yield larger correlations res <- rma.mv(yi, vi, mods = ~ spatially.pooled * temporally.pooled, random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy), R=list(species.id.phy=A), data=dat) res ### estimated average correlation without pooling, when pooling spatially, ### when pooling temporally, and when pooling spatially and temporally predict(res, newmods = rbind(c(0,0,0),c(1,0,0),c(0,1,0),c(1,1,1)), transf=transf.ztor, digits=2) } } \keyword{datasets} \concept{ecology} \concept{evolution} \concept{correlation coefficients} \concept{multivariate models} \concept{phylogeny} \concept{meta-regression} \section{Concepts}{ ecology, evolution, correlation coefficients, multivariate models, phylogeny, meta-regression } metadat/man/dat.crisafulli2020.Rd0000644000176200001440000000510414750466700016154 0ustar liggesusers\name{dat.crisafulli2020} \docType{data} \alias{dat.crisafulli2020} \title{Duchenne Muscular Dystrophy (DMD) Prevalence Data} \description{26 studies reporting estimates of the birth prevalence of Duchenne muscular dystrophy.} \usage{ dat.crisafulli2020 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label (first author, year) \cr \bold{pubyear} \tab \code{integer} \tab publication year \cr \bold{country} \tab \code{factor} \tab origin of investigated population \cr \bold{from}, \bold{to} \tab \code{integer} \tab time span of investigation (years) \cr \bold{cases} \tab \code{integer} \tab number of DMD cases \cr \bold{total} \tab \code{integer} \tab corresponding total population \cr } } \details{ \emph{Duchenne muscular dystrophy} (DMD) is a rare disease that is caused by a genetic mutation and is characterized by impairment through muscle weakness and a reduced life expectancy. Crisafulli et al. (2020) reported on a systematic review of data on the epidemiology of DMD, including estimates of the \emph{birth prevalence} (which is of the order of a few per ten thousand). One of the originally reported studies (Koenig, 2019) is omitted here, as it constitutes an obvious outlier, and the reliability of the reported data is doubtful; Crisafulli et al. (2020) pointed out that \dQuote{\emph{Concerning birth prevalence, Koenig et al. were found to be outliers. This study had problems with data collection in the last study year, as due to privacy issues, DMD cases were under-reported.}} } \source{ Crisafulli, S., Sultana, J., Fontana, A., Salvo, F., Messina, S., & Trifiro, G. (2020). Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta-analysis. \emph{Orphanet Journal of Rare Diseases}, \bold{15}, 141. \verb{https://doi.org/10.1186/s13023-020-01430-8} } \author{ Christian Roever, \email{christian.roever@med.uni-goettingen.de} } \examples{ # show (some) data head(dat.crisafulli2020) \dontrun{ # compute logarithmic proportions and associated standard errors library(metafor) logp <- escalc(measure="PLN", xi=cases, ni=total, slab=study, data=dat.crisafulli2020) # perform meta-analysis rma01 <- rma.uni(logp) # show results rma01 # illustrate in a forest plot forest(rma01, xlim=c(-12,-5)) } } \keyword{datasets} \concept{medicine} \concept{epidemiology} \concept{proportions} \concept{dose-response models} \section{Concepts}{ medicine, epidemiology, proportions, dose-response models } metadat/man/datsearch.Rd0000644000176200001440000001135614750466700014710 0ustar liggesusers\name{datsearch} \alias{datsearch} \title{Search Function for the Datasets} \description{Function to search among the existing datasets.} \usage{ datsearch(pattern, concept=TRUE, matchall=TRUE, fixed=TRUE, pkgdown=FALSE) } \arguments{ \item{pattern}{character string or vector of strings specifying the terms to search for within the datasets. Can also be left unspecified to start the function in an interactive mode.} \item{concept}{logical indicating whether the search should be confined to the concept terms (\code{TRUE} by default) or whether a full-text search should be conducted.} \item{matchall}{logical indicating whether only the datasets matching all terms (if multiple are specified) are returned (\code{TRUE} by default) or whether datasets matching any one of the terms are returned.} \item{fixed}{logical indicating whether a term is a string to be matched as is (\code{TRUE} by default). If \code{FALSE}, a search term is a regular expression that \code{\link{grep}} will search for. Only relevant when \code{concept=FALSE} (i.e., when doing a full-text search).} \item{pkgdown}{logical indicating whether the standard help file or the pkgdown docs (at \url{https://wviechtb.github.io/metadat/}) should be shown for a chosen dataset (\code{FALSE} by default).} } \details{ The function can be used to search all existing datasets in the \pkg{metadat} package based on their concept terms (see below) or based on a full-text search of their corresponding help files. When running \code{datsearch()} without the \code{pattern} argument specified, the function starts in an interactive mode and prompts for one or multiple search terms. Alternatively, one can specify a single search term via the \code{pattern} argument or multiple search terms by using a string vector as the \code{pattern} or by separating multiple search terms in a single string with \sQuote{,}, \sQuote{;}, or \sQuote{and}. If \code{matchall=TRUE} (the default), only datasets matching all search terms (if multiple are specified) are returned. If \code{matchall=FALSE}, datasets matching any one of the search terms are returned. If a single match is found, the corresponding help file is directly shown. If multiple matches are found, the user is prompted to choose one of the matching datasets of interest. \bold{Concept Terms} Each dataset is tagged with one or multiple concept terms that refer to various aspects of a dataset, such as the field/topic of research, the outcome measure used for the analysis, the model(s) used for analyzing the data, and the methods/concepts that can be illustrated with the dataset. \itemize{ \item In terms of \sQuote{fields/topics}, the following terms have been used at least once: alternative medicine, attraction, cardiology, climate change, covid-19, criminology, dentistry, ecology, education, engineering, epidemiology, evolution, genetics, human factors, medicine, memory, obstetrics, oncology, persuasion, physics, primary care, psychiatry, psychology, smoking, social work, sociology. \item In terms of \sQuote{outcome measures}, the following terms have been used at least once: correlation coefficients, Cronbach's alpha, hazard ratios, incidence rates, raw mean differences, odds ratios, proportions, ratios of means, raw means, risk differences, risk ratios, (semi-)partial correlations, standardized mean changes, standardized mean differences. \item In terms of \sQuote{models/methods/concepts}, the following terms have been used at least once: cluster-robust inference, component network meta-analysis, cumulative meta-analysis, diagnostic accuracy studies, dose response models, generalized linear models, longitudinal models, Mantel-Haenszel method, meta-regression, model checks, multilevel models, multivariate models, network meta-analysis, outliers, Peto's method, phylogeny, publication bias, reliability generalization, single-arm studies, spatial correlation, subgroup analysis. } } \author{ Daniel Noble, \email{daniel.noble@anu.edu.au} \cr Wolfgang Viechtbauer, \email{wvb@metafor-project.org} } \examples{ # note: the examples below are not run since they require interactivity if (FALSE) { # start the function in the interactive mode datsearch() # find all datasets tagged with the concept term 'standardized mean differences' datsearch("standardized mean differences") # find all datasets tagged with the concept terms 'odds ratio' and 'multilevel' datsearch("odds ratio, multilevel") # do a full-text search for the term 'infarct' datsearch("infarct", concept=FALSE) # do a full-text search for 'rma.mv(' (essentially finds all datasets where # the rma.mv() function was used in the examples section of a help file) datsearch("rma.mv(", concept=FALSE) } } \keyword{file} metadat/man/dat.mccurdy2020.Rd0000644000176200001440000001645014750466700015473 0ustar liggesusers\name{dat.mccurdy2020} \docType{data} \alias{dat.mccurdy2020} \title{Studies on the Generation Effect} \description{Results from 126 articles that examined the so-called \sQuote{generation effect}.} \usage{ dat.mccurdy2020 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{article} \tab \code{numeric} \tab article identifier \cr \bold{experiment} \tab \code{character} \tab experiment (within article) identifier \cr \bold{sample} \tab \code{numeric} \tab sample (within experiment) identifier \cr \bold{id} \tab \code{numeric} \tab row identifier \cr \bold{pairing} \tab \code{numeric} \tab identifier to indicate paired conditions within experiments \cr \bold{yi} \tab \code{numeric} \tab mean recall rate for the condition \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{ni} \tab \code{numeric} \tab number of participants for the condition \cr \bold{stimuli} \tab \code{numeric} \tab number of stimuli for the condition \cr \bold{condition} \tab \code{factor} \tab condition (\sQuote{read} or \sQuote{generate}) \cr \bold{gen_difficulty} \tab \code{factor} \tab generation difficulty (\sQuote{low} or \sQuote{high}) \cr \bold{manip_type} \tab \code{factor} \tab manipulation type of the generate versus read condition (using a \sQuote{within} or \sQuote{between} subjects design) \cr \bold{present_style} \tab \code{factor} \tab presentation style (\sQuote{mixed} or \sQuote{pure} list presentation) \cr \bold{word_status} \tab \code{factor} \tab word status (\sQuote{words}, \sQuote{non-words}, or \sQuote{numbers}) \cr \bold{memory_test} \tab \code{factor} \tab memory test (\sQuote{recognition}, \sQuote{cued recall}, or \sQuote{free recall}) \cr \bold{memory_type} \tab \code{factor} \tab memory type (\sQuote{item}, \sQuote{source}, \sQuote{font color}, \sQuote{font type}, \sQuote{order}, \sQuote{cue word}, \sQuote{background color}, or \sQuote{location}) \cr \bold{gen_constraint} \tab \code{factor} \tab generation constraint (\sQuote{low}, \sQuote{medium}, or \sQuote{high}) \cr \bold{learning_type} \tab \code{factor} \tab learning type (\sQuote{incidental} or \sQuote{intentional}) \cr \bold{stimuli_relation} \tab \code{factor} \tab stimuli relation (\sQuote{semantic}, \sQuote{category}, \sQuote{antonym}, \sQuote{synonym}, \sQuote{rhyme}, \sQuote{compound words}, \sQuote{definitions}, or \sQuote{unrelated}) \cr \bold{gen_mode} \tab \code{factor} \tab generation mode (\sQuote{verbal/speaking}, \sQuote{covert/thinking}, or \sQuote{writing/typing}) \cr \bold{gen_task} \tab \code{factor} \tab generation task (\sQuote{anagram}, \sQuote{letter transposition}, \sQuote{word fragment}, \sQuote{sentence completion}, \sQuote{word stem}, \sQuote{calculation}, or \sQuote{cue only}) \cr \bold{attention} \tab \code{factor} \tab attention (\sQuote{divided} or \sQuote{full}) \cr \bold{pacing} \tab \code{factor} \tab pacing (\sQuote{self-paced} or \sQuote{timed}) \cr \bold{filler_task} \tab \code{factor} \tab filler task (\sQuote{yes} or \sQuote{no}) \cr \bold{age_grp} \tab \code{factor} \tab age group (\sQuote{younger} or \sQuote{older} adults) \cr \bold{retention_delay} \tab \code{factor} \tab retention delay (\sQuote{immediate}, \sQuote{short}, or \sQuote{long}) \cr } } \details{ The generation effect is the memory benefit for self-generated compared with read or experimenter-provided information (Jacoby, 1978; Slamecka & Graf, 1978). In a typical study, participants are presented with a list of stimuli (usually words or word pairs). For half of the stimuli, participants self-generate a target word (e.g., open–cl____), while for the other half, participants simply read an intact target word (e.g., above–below). On a later memory test for the target words, the common finding is that self-generated words are better remembered than read words (i.e., the generation effect). Although several theories have been proposed to explain the generation effect, there is still some debate on the underlying memory mechanism(s) contributing to this phenomenon. The meta-analysis by McCurdy et al. (2020) translated various theories on the generation effect into hypotheses that could then be tested in moderator analyses based on a dataset containing 126 articles, 310 experiments, and 1653 mean recall estimates collected under various conditions. Detailed explanations of the various variables coded (and how these can be used to test various hypotheses regarding the generation effect) can be found in the article. The most important variable is \code{condition}, which denotes whether a particular row of the dataset corresponds to the results of a \sQuote{read} or a \sQuote{generate} condition. The data structure is quite complex. Articles may have reported the findings from multiple experiments involving one or multiple samples that were examined under various conditions. The \code{pairing} variable indicates which rows of the dataset represent a pairing of a read condition with one or multiple corresponding generate conditions within an experiment. A pairing may involve the same sample of subjects (when using a within-subjects design for comparing the conditions) or different samples (when using a between-subjects design). } \source{ McCurdy, M. P., Viechtbauer, W., Sklenar, A. M., Frankenstein, A. N., & Leshikar, E. D. (2020). Theories of the generation effect and the impact of generation constraint: A meta-analytic review. \emph{Psychonomic Bulletin & Review}, \bold{27}(6), 1139--1165. \verb{https://doi.org/10.3758/s13423-020-01762-3} } \references{ Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. \emph{Journal of Experimental Psychology: Human Learning and Memory}, \bold{4}(6), 592--604. \verb{https://doi.org/10.1037/0278-7393.4.6.592} Jacoby, L. L. (1978). On interpreting the effects of repetition: Solving a problem versus remembering a solution. \emph{Journal of Verbal Learning and Verbal Behavior}, \bold{17}(6), 649--668. \verb{https://doi.org/10.1016/S0022-5371(78)90393-6} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.mccurdy2020 head(dat) \dontrun{ ### load metafor package library(metafor) ### fit multilevel mixed-effects meta-regression model res <- rma.mv(yi, vi, mods = ~ condition, random = list(~ 1 | article/experiment/sample/id, ~ 1 | pairing), data=dat, sparse=TRUE, digits=3) res ### proportion of total amount of heterogeneity due to each component data.frame(source=res$s.names, sigma2=round(res$sigma2, 3), prop=round(res$sigma2 / sum(res$sigma2), 2)) ### apply cluster-robust inference methods sav <- robust(res, cluster=article, clubSandwich=TRUE) sav ### estimated average recall rate in read and generate conditions predict(sav, newmods = c(0,1), digits=3) } } \keyword{datasets} \concept{psychology} \concept{memory} \concept{proportions} \concept{raw means} \concept{multilevel models} \concept{cluster-robust inference} \section{Concepts}{ psychology, memory, proportions, raw means, multilevel models, cluster-robust inference } metadat/man/dat.hasselblad1998.Rd0000644000176200001440000001543414750466700016157 0ustar liggesusers\name{dat.hasselblad1998} \docType{data} \alias{dat.hasselblad1998} \title{Studies on the Effectiveness of Counseling for Smoking Cessation} \description{Results from 24 studies on the effectiveness of various counseling types for smoking cessation.} \usage{ dat.hasselblad1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab id number for each treatment arm \cr \bold{study} \tab \code{numeric} \tab study id number \cr \bold{authors} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{trt} \tab \code{character} \tab intervention group \cr \bold{xi} \tab \code{numeric} \tab number of individuals abstinent \cr \bold{ni} \tab \code{numeric} \tab number of individuals in group } } \details{ The dataset includes the results from 24 studies on the effectiveness of various counseling types for smoking cessation (i.e., self-help, individual counseling, group counseling, and no contact). The dataset indicates the total number of individuals within each study arm and the number that were abstinent from 6 to 12 months. The majority of the studies compared two interventions types against each other, while 2 studies compared three types against each other simultaneously. The data can be used for a \sQuote{network meta-analysis} (also called a \sQuote{mixed treatment comparison}). The code below shows how such an analysis can be conducted using an arm-based and a contrast-based model (see Salanti et al., 2008, for more details). } \source{ Hasselblad, V. (1998). Meta-analysis of multitreatment studies. \emph{Medical Decision Making}, \bold{18}(1), 37--43. \verb{https://doi.org/10.1177/0272989X9801800110} } \references{ Gleser, L. J., & Olkin, I. (2009). Stochastically dependent effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), \emph{The handbook of research synthesis and meta-analysis} (2nd ed., pp. 357--376). New York: Russell Sage Foundation. Law, M., Jackson, D., Turner, R., Rhodes, K., & Viechtbauer, W. (2016). Two new methods to fit models for network meta-analysis with random inconsistency effects. \emph{BMC Medical Research Methodology}, \bold{16}, 87. \verb{https://doi.org/10.1186/s12874-016-0184-5} Salanti, G., Higgins, J. P. T., Ades, A. E., & Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. \emph{Statistical Methods in Medical Research}, \bold{17}(3), 279--301. \verb{https://doi.org/10.1177/0962280207080643} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hasselblad1998 dat \dontrun{ ### load metafor package library(metafor) ### create network graph ('igraph' package must be installed) library(igraph, warn.conflicts=FALSE) pairs <- data.frame(do.call(rbind, sapply(split(dat$trt, dat$study), function(x) t(combn(x,2)))), stringsAsFactors=FALSE) lvls <- c("no_contact", "self_help", "ind_counseling", "grp_counseling") pairs$X1 <- factor(pairs$X1, levels=lvls) pairs$X2 <- factor(pairs$X2, levels=lvls) tab <- table(pairs[,1], pairs[,2]) tab # adjacency matrix g <- graph_from_adjacency_matrix(tab, mode = "plus", weighted=TRUE, diag=FALSE) vertex_attr(g, "name") <- c("No Contact", "Self-Help", "Individual\nCounseling", "Group\nCounseling") plot(g, edge.curved=FALSE, edge.width=E(g)$weight, layout=layout_on_grid, vertex.size=45, vertex.color="lightgray", vertex.label.color="black", vertex.label.font=2) ### calculate log odds for each study arm dat <- escalc(measure="PLO", xi=xi, ni=ni, add=1/2, to="all", data=dat) dat ### convert trt variable to factor with desired ordering of levels dat$trt <- factor(dat$trt, levels=c("no_contact", "self_help", "ind_counseling", "grp_counseling")) ### add a space before each level (this makes the output a bit more legible) levels(dat$trt) <- paste0(" ", levels(dat$trt)) ### network meta-analysis using an arm-based model with fixed study effects ### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons res <- rma.mv(yi, vi, mods = ~ 0 + factor(study) + trt, random = ~ trt | study, rho=1/2, data=dat, btt="trt") res ### all pairwise odds ratios of interventions versus no contact predict(res, newmods=cbind(matrix(0, nrow=3, ncol=24), diag(3)), intercept=FALSE, transf=exp, digits=2) ### all pairwise odds ratios comparing interventions (ic vs sh, gc vs sh, and gc vs ic) predict(res, newmods=cbind(matrix(0, nrow=3, ncol=24), rbind(c(-1,1,0), c(-1,0,1), c(0,-1,1))), intercept=FALSE, transf=exp, digits=2) ### forest plot of ORs of interventions versus no contact forest(c(0,res$beta[25:27]), sei=c(0,res$se[25:27]), psize=1, xlim=c(-3,4), digits=c(2,1), efac=2, slab=c("No Contact", "Self-Help", "Individual Counseling", "Group Counseling"), atransf=exp, at=log(c(0.5, 1, 2, 4, 8)), xlab="Odds Ratio for Intervention vs. No Contact", header=c("Intervention", "Odds Ratio [95\% CI]")) ############################################################################ ### restructure dataset to a contrast-based format dat <- to.wide(dat.hasselblad1998, study="study", grp="trt", ref="no_contact", grpvars=6:7) ### calculate log odds ratios for each treatment comparison dat <- escalc(measure="OR", ai=xi.1, n1i=ni.1, ci=xi.2, n2i=ni.2, add=1/2, to="all", data=dat) dat ### calculate the variance-covariance matrix of the log odds ratios for multitreatment studies ### see Gleser & Olkin (2009), equation (19.11), for the covariance equation calc.v <- function(x) { v <- matrix(1/(x$xi.2[1] + 1/2) + 1/(x$ni.2[1] - x$xi.2[1] + 1/2), nrow=nrow(x), ncol=nrow(x)) diag(v) <- x$vi v } V <- bldiag(lapply(split(dat, dat$study), calc.v)) ### add contrast matrix to dataset dat <- contrmat(dat, grp1="trt.1", grp2="trt.2") dat ### network meta-analysis using a contrast-based random-effects model ### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons res <- rma.mv(yi, V, mods = ~ 0 + self_help + ind_counseling + grp_counseling, random = ~ comp | study, rho=1/2, data=dat) res ### predicted odds ratios of interventions versus no contact predict(res, newmods=diag(3), transf=exp, digits=2) ### fit random inconsistency effects model (see Law et al., 2016) res <- rma.mv(yi, V, mods = ~ 0 + self_help + ind_counseling + grp_counseling, random = list(~ comp | study, ~ comp | design), rho=1/2, phi=1/2, data=dat) res } } \keyword{datasets} \concept{medicine} \concept{psychology} \concept{smoking} \concept{odds ratios} \concept{network meta-analysis} \section{Concepts}{ medicine, psychology, smoking, odds ratios, network meta-analysis } metadat/man/dat.dumouchel1994.Rd0000644000176200001440000000711214750466700016030 0ustar liggesusers\name{dat.dumouchel1994} \docType{data} \alias{dat.dumouchel1994} \title{Nitrogen dioxide data set} \description{Nine studies investigating the effect of NO2 exposure on respiratory illness in children.} \usage{ dat.dumouchel1994 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{smoke} \tab \code{factor} \tab adjustment for smoking (y/n) \cr \bold{no2} \tab \code{factor} \tab direct measurement of NO2 concentration (y/n) \cr \bold{gender} \tab \code{factor} \tab adjustment for gender (y/n) \cr \bold{or} \tab \code{numeric} \tab odds ratio for childhood respiratory illness \cr \bold{lower} \tab \code{numeric} \tab lower bound of 95 percent CI \cr \bold{upper} \tab \code{numeric} \tab upper bound of 95 percent CI } } \details{ Hasselblad et al. (1992) investigated the effects of nitrogen dioxide (NO2) exposure on the occurrence of respiratory illness in children. Their data were picked up by DuMouchel (1994) as an illustrative example in his article on Bayesian meta-analysis, and were also part of his \dQuote{\code{hblm}} S-Plus software package. DuMouchel's dataset differs slightly from the figures quoted by Hasselblad et al. (1992), apparently because he had additional, more detailed data available. The data set features three study-level covariables reflecting characteristics of the study designs, namely, whether the quoted estimate had been adjusted for parents' smoking status, whether NO2 exposure had been measured directly (or presence of a gas stove in the household had been used as a proxy instead), and whether the quoted effect had been adjusted for gender. Inclusion of the covariables allows to account for the studies' design features, quantify their effects, and adjust for these. } \source{ DuMouchel, W. H. (1994). Hierarchical Bayes linear models for meta-analysis. Technical Report 27, National Institute of Statistical Sciences (NISS); Research Triangle Park, NC, USA. \verb{https://www.niss.org/research/technical-reports/hierarchical-bayes-linear-models-meta-analysis-1994} } \references{ Hasselblad, V., Eddy, D. M., & Kotchmar, D. J. (1992). Synthesis of environmental evidence: Nitrogen dioxide epidemiology studies. \emph{Journal of the Air and Waste Management Association}, \bold{42}(5), 662--671. \verb{https://doi.org/10.1080/10473289.1992.10467018} } \author{ Christian Roever, \email{christian.roever@med.uni-goettingen.de} } \examples{ # show data: dat.dumouchel1994 \dontrun{ # derive effect sizes (log-ORs): library(metafor) no2 <- escalc(measure="OR", yi=log(or), sei=(log(upper)-log(lower))/(2*qnorm(0.975)), slab=study, data=dat.dumouchel1994) summary(no2) # compute overall meta-analysis: library(bayesmeta) bm01 <- bayesmeta(no2, tau.prior="DuMouchel") # show results: bm01 forestplot(bm01) traceplot(bm01) # perform meta-regression; # specify regressor matrix: X <- model.matrix( ~ smoke + no2 + gender, data=no2) colnames(X) <- c("intercept", "smoke", "no2", "gender") # perform regression: bm02 <- bmr(no2, X=X, tau.prior="DuMouchel") # show results: bm02 forestplot(bm02) #forestplot(bm02, xlab="log-OR", # X.mean=rbind("none" = c(1,0,0,0), # "smoke" = c(1,1,0,0), # "no2" = c(1,0,1,0), # "gender" = c(1,0,0,1), # "all three" = c(1,1,1,1))) traceplot(bm02) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{meta-regression} \section{Concepts}{ medicine, odds ratios, meta-regression } metadat/man/dat.lim2014.Rd0000644000176200001440000001033414750466700014604 0ustar liggesusers\name{dat.lim2014} \docType{data} \alias{dat.lim2014} \title{Studies on the Association Between Maternal Size, Offspring Size, and Number of Offsprings} \description{Results from studies examining the association between maternal size, offspring size, and number of offsprings.} \usage{ dat.lim2014 } \format{ The object is a list containing data frames \code{m_o_size}, \code{m_o_fecundity}, \code{o_o_unadj}, and \code{o_o_adj} that contain the following columns and the corresponding phylogenetic trees called \code{m_o_size_tree}, \code{m_o_fecundity_tree}, \code{o_o_unadj_tree}, and \code{o_o_adj_tree}: \tabular{lll}{ \bold{article} \tab \code{numeric} \tab article id \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{species} \tab \code{character} \tab species \cr \bold{amniotes} \tab \code{character} \tab whether the species was amniotic \cr \bold{environment} \tab \code{character} \tab whether the species were wild or captive \cr \bold{reprounit} \tab \code{character} \tab whether the data were based on lifetime reproductive output or a single reproductive event (only in \code{m_o_size} and \code{m_o_fecundity}) \cr \bold{ri} \tab \code{numeric} \tab correlation coefficient \cr \bold{ni} \tab \code{numeric} \tab sample size } } \details{ The object \code{dat.lim2014} includes 4 datasets: \tabular{ll}{ \code{m_o_size} \tab on the correlation between maternal size and offspring size \cr \code{m_o_fecundity} \tab on the correlation between maternal size and number of offsprings \cr \code{o_o_unadj} \tab on the correlation between offspring size and number of offsprings \cr \code{o_o_adj} \tab on the correlation between offspring size and number of offsprings adjusted for maternal size } Objects \code{m_o_size_tree}, \code{m_o_fecundity_tree}, \code{o_o_unadj_tree}, and \code{o_o_adj_tree} are the corresponding phylogenetic trees for the species included in each of these datasets. } \source{ Lim, J. N., Senior, A. M., & Nakagawa, S. (2014). Heterogeneity in individual quality and reproductive trade-offs within species. \emph{Evolution}, \bold{68}(8), 2306--2318. \verb{https://doi.org/10.1111/evo.12446} } \references{ Cinar, O., Nakagawa, S., & Viechtbauer, W. (in press). Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. \emph{Methods in Ecology and Evolution}. \verb{https://doi.org/10.1111/2041-210X.13760} Hadfield, J. D., & Nakagawa, S. (2010). General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. \emph{Journal of Evolutionary Biology}, \bold{23}(3), 494--508. \verb{https://doi.org/10.1111/j.1420-9101.2009.01915.x} Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. \emph{Evolutionary Ecology}, \bold{26}(5), 1253--1274. \verb{https://doi.org/10.1007/s10682-012-9555-5} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.lim2014$o_o_unadj dat[1:14, -c(2:3)] \dontrun{ ### load metafor package library(metafor) ### load ape package library(ape, warn.conflicts=FALSE) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat) ### copy tree to 'tree' tree <- dat.lim2014$o_o_unadj_tree ### compute branch lengths tree <- compute.brlen(tree) ### compute phylogenetic correlation matrix A <- vcv(tree, corr=TRUE) ### make copy of the species variable dat$species.phy <- dat$species ### create effect size id variable dat$esid <- 1:nrow(dat) ### fit multilevel phylogenetic meta-analytic model res <- rma.mv(yi, vi, random = list(~ 1 | article, ~ 1 | esid, ~ 1 | species, ~ 1 | species.phy), R=list(species.phy=A), data=dat) res } } \keyword{datasets} \concept{ecology} \concept{evolution} \concept{correlation coefficients} \concept{multilevel models} \concept{phylogeny} \section{Concepts}{ ecology, evolution, correlation coefficients, multilevel models, phylogeny } metadat/man/dat.franchini2012.Rd0000644000176200001440000000726614750466700015774 0ustar liggesusers\name{dat.franchini2012} \docType{data} \alias{dat.franchini2012} \title{Studies on Dopamine Agonists to Reduce \dQuote{Off-Time} in Patients with Advanced Parkinson Disease} \description{Results from 7 trials examining the effectiveness of four dopamine agonists and placebo to reduce \dQuote{off-time} in patients with advanced Parkinson disease.} \usage{ dat.franchini2012 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{Study} \tab \code{character} \tab study label \cr \bold{Treatment1} \tab \code{character} \tab treatment 1 \cr \bold{y1} \tab \code{numeric} \tab treatment effect arm 1 \cr \bold{sd1} \tab \code{numeric} \tab standard deviation arm 2 \cr \bold{n1} \tab \code{integer} \tab sample size arm 1 \cr \bold{Treatment2} \tab \code{character} \tab treatment 2 \cr \bold{y2} \tab \code{numeric} \tab treatment effect arm 2 \cr \bold{sd2} \tab \code{numeric} \tab standard deviation arm 2 \cr \bold{n2} \tab \code{integer} \tab sample size arm 1 \cr \bold{Treatment3} \tab \code{character} \tab treatment 3 \cr \bold{y3} \tab \code{numeric} \tab treatment effect arm 3 \cr \bold{sd3} \tab \code{numeric} \tab standard deviation arm 2 \cr \bold{n3} \tab \code{integer} \tab sample size arm 1 } } \details{ This network meta-analysis compared the effectiveness of four active treatments and placebo in patients with advanced Parkinson disease (Franchini et al., 2012). The outcome is mean lost work-time reduction in patients given dopamine agonists as adjunct therapy. The data are given as sample size, mean, and standard deviation in each trial arm. This dataset was used as an example in the supplemental material of Dias et al. (2013) where placebo is coded as 1 and the four active drugs as 2 to 5. } \source{ Dias, S., Sutton, A. J., Ades, A. E., & Welton, N. J. (2013). Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. \emph{Medical Decision Making}, \bold{33}(5), 607--617. \verb{https://doi.org/10.1177/0272989X12458724} Franchini, A. J., Dias, S., Ades, A. E., Jansen, J. P., & Welton, N. J. (2012). Accounting for correlation in network meta-analysis with multi-arm trials. \emph{Research Synthesis Methods}, \bold{3}(2), 142--160. \verb{https://doi.org/10.1002/jrsm.1049} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show results from first three studies; third study is a three-arm ### study head(dat.franchini2012, 3) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print mean differences with two digits oldset <- settings.meta(digits = 2) ### Transform data from wide arm-based format to contrast-based ### format. Argument 'sm' must not be provided as the mean difference ### is the default in R function metacont() called internally. pw <- pairwise(list(Treatment1, Treatment2, Treatment3), n = list(n1, n2, n3), mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3), data = dat.franchini2012, studlab = Study, sm = "MD") ### Show calculated mean differences (TE) for first three studies pw[1:5, c(3:7, 10, 1)] ### Conduct network meta-analysis net <- netmeta(pw) net ### Draw network graph netgraph(net, points = TRUE, cex.points = 3, cex = 1.5, plastic = TRUE, thickness = "se.fixed", iterate = TRUE, start = "eigen") ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{raw mean differences} \concept{network meta-analysis} \section{Concepts}{ medicine, raw mean differences, network meta-analysis } metadat/man/dat.hart1999.Rd0000644000176200001440000000670514750466700015015 0ustar liggesusers\name{dat.hart1999} \docType{data} \alias{dat.hart1999} \title{Studies on the Effectiveness of Warfarin for Preventing Strokes} \description{Results from 6 clinical trials examining the effectiveness of adjusted-dose warfarin for preventing strokes in patients with atrial fibrillation.} \usage{ dat.hart1999 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{numeric} \tab trial number \cr \bold{study} \tab \code{character} \tab study name (abbreviated) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{x1i} \tab \code{numeric} \tab number of strokes in the warfarin group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the warfarin group \cr \bold{t1i} \tab \code{numeric} \tab total person-time (in years) in the warfarin group \cr \bold{x2i} \tab \code{numeric} \tab number of strokes in the placebo/control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the placebo/control group \cr \bold{t2i} \tab \code{numeric} \tab total person-time (in years) in the placebo/control group \cr \bold{compgrp} \tab \code{character} \tab type of comparison group (placebo or control) \cr \bold{prevtype} \tab \code{character} \tab type of prevention (primary or secondary) \cr \bold{trinr} \tab \code{character} \tab target range for the international normalized ratio (INR) } } \details{ The 6 studies provide data with respect to the number of strokes in the warfarin and the comparison (placebo or control) group. In addition, the number of patients and the total person-time (in years) is provided for the two groups. The goal of the meta-analysis was to examine the effectiveness of adjusted-dose warfarin for preventing strokes in patients with atrial fibrillation. } \source{ Hart, R. G., Benavente, O., McBride, R., & Pearce, L. A. (1999). Antithrombotic therapy to prevent stroke in patients with atrial fibrillation: A meta-analysis. \emph{Annals of Internal Medicine}, \bold{131}(7), 492--501. \verb{https://doi.org/10.7326/0003-4819-131-7-199910050-00003} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hart1999 dat \dontrun{ ### load metafor package library(metafor) ### calculate log incidence rate ratios and corresponding sampling variances dat <- escalc(measure="IRR", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat) dat ### meta-analysis of log incidence rate ratios using a random-effects model res <- rma(yi, vi, data=dat) res ### average incidence rate ratio with 95\% CI predict(res, transf=exp) ### forest plot with extra annotations par(mar=c(5,4,1,2)) forest(res, xlim=c(-11, 5), at=log(c(0.05, 0.25, 1, 4)), atransf=exp, slab=paste0(study, " (", year, ")"), ilab=cbind(paste(x1i, "/", t1i, sep=" "), paste(x2i, "/", t2i, sep=" ")), ilab.xpos=c(-6.5,-4), cex=0.85, header="Study (Year)") op <- par(cex=0.85, font=2) text(c(-6.5,-4), 8.5, c("Warfarin", "Control")) text(c(-6.5,-4), 7.5, c("Strokes / PT", "Strokes / PT")) segments(x0=-8, y0=8, x1=-2.75, y1=8) par(op) ### meta-analysis of incidence rate differences using a random-effects model res <- rma(measure="IRD", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat) res } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{incidence rates} \section{Concepts}{ medicine, cardiology, incidence rates } metadat/man/dat.berkey1998.Rd0000644000176200001440000001046214750466700015332 0ustar liggesusers\name{dat.berkey1998} \docType{data} \alias{dat.berkey1998} \title{Studies on Treatments for Periodontal Disease} \description{Results from 5 trials comparing surgical and non-surgical treatments for medium-severity periodontal disease one year after treatment. \loadmathjax} \usage{ dat.berkey1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{numeric} \tab trial number \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ni} \tab \code{numeric} \tab number of patients \cr \bold{outcome} \tab \code{character} \tab outcome (PD = probing depth; AL = attachment level) \cr \bold{yi} \tab \code{numeric} \tab observed mean difference in outcome (surgical versus non-surgical) \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{v1i} \tab \code{numeric} \tab variances and covariances of the observed effects \cr \bold{v2i} \tab \code{numeric} \tab variances and covariances of the observed effects } } \details{ The dataset includes the results from 5 trials that compared surgical and non-surgical methods for the treatment of medium-severity periodontal disease. Reported outcomes include the change in probing depth (PD) and attachment level (AL) one year after the treatment. The outcome measure used for this meta-analysis was the (raw) mean difference, calculated in such a way that positive values indicate that surgery was more effective than non-surgical treatment in decreasing the probing depth and increasing the attachment level (so, the results from the various trials indicate that surgery is preferable for reducing the probing depth, while non-surgical treatment is preferable for increasing the attachment level). Since each trial provides effect size estimates for both outcomes, the estimates are correlated. A multivariate model can be used to meta-analyze the two outcomes simultaneously. The \code{v1i} and \code{v2i} values are the variances and covariances of the observed effects. In particular, for each study, variables \code{v1i} and \code{v2i} form a \mjeqn{2 \times 2}{2x2} variance-covariance matrix of the observed effects, with the diagonal elements corresponding to the sampling variances of the mean differences (the first for probing depth, the second for attachment level) and the off-diagonal value corresponding to the covariance of the two mean differences. Below, the full (block diagonal) variance-covariance for all studies is constructed from these two variables. } \source{ Berkey, C. S., Antczak-Bouckoms, A., Hoaglin, D. C., Mosteller, F., & Pihlstrom, B. L. (1995). Multiple-outcomes meta-analysis of treatments for periodontal disease. \emph{Journal of Dental Research}, \bold{74}(4), 1030--1039. \verb{https://doi.org/10.1177/00220345950740040201} Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. \emph{Statistics in Medicine}, \bold{17}(22), 2537--2550. \verb{https://doi.org/10.1002/(sici)1097-0258(19981130)17:22<2537::aid-sim953>3.0.co;2-c} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.berkey1998 dat \dontrun{ ### load metafor package library(metafor) ### construct block diagonal var-cov matrix of the observed outcomes based on variables v1i and v2i V <- vcalc(vi=1, cluster=author, rvars=c(v1i, v2i), data=dat) ### fit multiple outcomes (meta-regression) model (with REML estimation) res <- rma.mv(yi, V, mods = ~ 0 + outcome, random = ~ outcome | trial, struct="UN", data=dat) print(res, digits=3) ### test/estimate difference between the two outcomes anova(res, X=c(1,-1)) ### fit model including publication year as moderator for both outcomes (with ML estimation) res <- rma.mv(yi, V, mods = ~ 0 + outcome + outcome:I(year - 1983), random = ~ outcome | trial, struct="UN", data=dat, method="ML") print(res, digits=3) } } \keyword{datasets} \concept{medicine} \concept{dentistry} \concept{raw mean differences} \concept{multivariate models} \section{Concepts}{ medicine, dentistry, raw mean differences, multivariate models } metadat/man/dat.knapp2017.Rd0000644000176200001440000001461514750466700015145 0ustar liggesusers\name{dat.knapp2017} \docType{data} \alias{dat.knapp2017} \title{Studies on Differences in Planning Performance in Schizophrenia Patients versus Healthy Controls} \description{Results from 31 studies examining differences in planning performance in schizophrenia patients versus healthy controls.} \usage{ dat.knapp2017 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{study} \tab \code{numeric} \tab study id number \cr \bold{task} \tab \code{character} \tab type of task \cr \bold{difficulty} \tab \code{numeric} \tab task difficulty \cr \bold{group1} \tab \code{character} \tab identifier for patient group within studies \cr \bold{group2} \tab \code{character} \tab identifier for control group within studies \cr \bold{comp} \tab \code{numeric} \tab identifier for comparisons within studies \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference for planning performance \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{n_sz} \tab \code{numeric} \tab number of schizophrenic patients \cr \bold{n_hc} \tab \code{numeric} \tab number of healthy controls \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference for IQ \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance } } \details{ The studies included in this dataset examined differences between schizophrenia patients and healthy controls with respect to their performance on the tower of London test (\url{https://en.wikipedia.org/wiki/Tower_of_London_test}) or a similar cognitive tasks measuring planning ability. The outcome measure for this meta-analysis was the standardized mean difference (with positive values indicating better performance in the healthy controls compared to the schizophrenia patients). The dataset has a more complex structure for several reasons: \enumerate{ \item Studies 2, 3, 9, and 20 included more than one schizophrenia patient group and the standardized mean differences were computed by comparing these groups against a single healthy control group. \item Studies 6, 12, 14, 15, 18, 19, 22, and 26 had the patients and controls complete different tasks of varying complexity (essentially the average number of moves required to complete a task). Study 6 also included two different task types. \item Study 24 provides two standardized mean differences, one for men and the other for women. \item Study 29 provides three standardized mean differences, corresponding to the three different COMT Val158Met genotypes (val/val, val/met, and met/met). } All 4 issues described above lead to a multilevel structure in the dataset, with multiple standardized mean differences nested within some of the studies. Issues 1. and 2. also lead to correlated sampling errors. } \source{ Knapp, F., Viechtbauer, W., Leonhart, R., Nitschke, K., & Kaller, C. P. (2017). Planning performance in schizophrenia patients: A meta-analysis of the influence of task difficulty and clinical and sociodemographic variables. \emph{Psychological Medicine}, \bold{47}(11), 2002--2016. \verb{https://doi.org/10.1017/S0033291717000459} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.knapp2017 dat[-c(1:2)] \dontrun{ ### load metafor package library(metafor) ### fit a standard random-effects model ignoring the issues described above res <- rma(yi, vi, data=dat) res ### fit a multilevel model with random effects for studies and comparisons within studies ### (but this ignored the correlation in the sampling errors) res <- rma.mv(yi, vi, random = ~ 1 | study/comp, data=dat) res ### create variable that indicates the task and difficulty combination as increasing integers dat$task.diff <- unlist(lapply(split(dat, dat$study), function(x) { task.int <- as.integer(factor(x$task)) diff.int <- as.integer(factor(x$difficulty)) diff.int[is.na(diff.int)] <- 1 paste0(task.int, ".", diff.int)})) ### construct correlation matrix for two tasks with four different difficulties where the ### correlation is 0.4 for different difficulties of the same task, 0.7 for the same ### difficulty of different tasks, and 0.28 for different difficulties of different tasks R <- matrix(0.4, nrow=8, ncol=8) R[5:8,1:4] <- R[1:4,5:8] <- 0.28 diag(R[1:4,5:8]) <- 0.7 diag(R[5:8,1:4]) <- 0.7 diag(R) <- 1 rownames(R) <- colnames(R) <- paste0(rep(1:2, each=4), ".", 1:4) R ### construct an approximate V matrix accounting for the use of shared groups and ### for correlations among tasks/difficulties as specified in the R matrix above V <- vcalc(vi, cluster=study, grp1=group1, grp2=group2, w1=n_sz, w2=n_hc, obs=task.diff, rho=R, data=dat) ### correlation matrix for study 3 with four patient groups and a single control group round(cov2cor(V[dat$study == 3, dat$study == 3]), 2) ### correlation matrix for study 6 with two tasks with four difficulties cov2cor(V[dat$study == 6, dat$study == 6]) ### correlation matrix for study 24 with two independent groups cov2cor(V[dat$study == 24, dat$study == 24]) ### correlation matrix for study 29 with three independent groups cov2cor(V[dat$study == 29, dat$study == 29]) ### fit multilevel model as above, but now use this V matrix in the model res <- rma.mv(yi, V, random = ~ 1 | study/comp, data=dat) res predict(res, digits=2) ### use cluster-robust inference methods based on this model robust(res, cluster=study) ### use methods from the clubSandwich package robust(res, cluster=study, clubSandwich=TRUE) ### examine if task difficulty is a potential moderator of the effect res <- rma.mv(yi, V, mods = ~ difficulty, random = ~ 1 | study/comp, data=dat) res sav <- robust(res, cluster=study) sav sav <- robust(res, cluster=study, clubSandwich=TRUE) sav ### draw bubble plot regplot(sav, xlab="Task Difficulty", ylab="Standardized Mean Difference", las=1, digits=1, bty="l") } } \keyword{datasets} \concept{psychology} \concept{standardized mean differences} \concept{multilevel models} \concept{multivariate models} \concept{cluster-robust inference} \concept{meta-regression} \section{Concepts}{ psychology, standardized mean differences, multilevel models, multivariate models, cluster-robust inference, meta-regression } metadat/man/dat.nielweise2008.Rd0000644000176200001440000000634214750466700016016 0ustar liggesusers\name{dat.nielweise2008} \docType{data} \alias{dat.nielweise2008} \title{Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections} \description{Results from 18 studies comparing the risk of catheter-related bloodstream infection when using anti-infective-treated versus standard catheters for total parenteral nutrition or chemotherapy.} \usage{ dat.nielweise2008 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{authors} \tab \code{character} \tab study authors \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{x1i} \tab \code{numeric} \tab number of CRBSIs in patients receiving an anti-infective catheter \cr \bold{t1i} \tab \code{numeric} \tab total number of catheter days for patients receiving an anti-infective catheter \cr \bold{x2i} \tab \code{numeric} \tab number of CRBSIs in patients receiving a standard catheter \cr \bold{t2i} \tab \code{numeric} \tab total number of catheter days for patients receiving a standard catheter } } \details{ The use of a central venous catheter may lead to a catheter-related bloodstream infection (CRBSI), which in turn increases the risk of morbidity and mortality. Anti-infective-treated catheters have been developed that are meant to reduce the risk of CRBSIs. Niel-Weise et al. (2008) conducted a meta-analysis of studies comparing infection risk when using anti-infective-treated versus standard catheters for total parenteral nutrition or chemotherapy. The results from 9 such studies are included in this dataset. The dataset was used in the article by Stijnen et al. (2010) to illustrate various generalized linear mixed-effects models for the meta-analysis of incidence rates and incidence rate ratios (see \sQuote{References}). } \source{ Niel-Weise, B. S., Stijnen, T., & van den Broek, P. J. (2008). Anti-infective-treated central venous catheters for total parenteral nutrition or chemotherapy: A systematic review. \emph{Journal of Hospital Infection}, \bold{69}(2), 114--123. \verb{https://doi.org/10.1016/j.jhin.2008.02.020} } \references{ Stijnen, T., Hamza, T. H., & Ozdemir, P. (2010). Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. \emph{Statistics in Medicine}, \bold{29}(29), 3046--3067. \verb{https://doi.org/10.1002/sim.4040} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.nielweise2008 dat \dontrun{ ### load metafor package library(metafor) ### standard (inverse-variance) random-effects model res <- rma(measure="IRR", x1i=x1i, t1i=t1i, x2i=x2i, t2i=t2i, data=dat) print(res, digits=3) predict(res, transf=exp, digits=2) ### random-effects conditional Poisson model res <- rma.glmm(measure="IRR", x1i=x1i, t1i=t1i, x2i=x2i, t2i=t2i, data=dat, model="CM.EL") print(res, digits=3) predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{incidence rates} \concept{generalized linear models} \section{Concepts}{ medicine, incidence rates, generalized linear models } metadat/man/dat.bartos2023.Rd0000644000176200001440000001453414750466700015323 0ustar liggesusers\name{dat.bartos2023} \docType{data} \alias{dat.bartos2023} \title{Results of 350,757 Coin Flips to Examine Same-Side Bias} \description{Results from 350,757 coin flips by 48 people to examine the presence of same-side bias.} \usage{ dat.bartos2023 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{person} \tab \code{character} \tab person identifier \cr \bold{hsame} \tab \code{numeric} \tab number of flips where the coin landed on heads and on the same side as where it started \cr \bold{hdiff} \tab \code{numeric} \tab number of flips where the coin landed on heads and on the different side as where it started \cr \bold{tsame} \tab \code{numeric} \tab number of flips where the coin landed on tails and on the same side as where it started \cr \bold{tdiff} \tab \code{numeric} \tab number of flips where the coin landed on tails and on the different side as where it started \cr \bold{same} \tab \code{numeric} \tab number of flips where the coin landed on the same side as where it started \cr \bold{flips} \tab \code{numeric} \tab total number of flips } } \details{ In a landmark study by \enc{Bartoš}{Bartos} et al. (2023), 48 people flipped a coin (of various currencies and/or denominations) a total of 350,757 times, recording on each flip whether it landed on heads or tails and whether the coin landed on the same side as where it started or on the different side. The goal of this experiment was to examine the model by Diaconis, Holmes, and Montgomery (2007), according to which flipped coins have a slightly higher than 50\% chance (of around 51\% according to the D-H-M model) of landing on the same side as where they started. } \source{ \enc{Bartoš}{Bartos}, F., Sarafoglou, A., Godmann, H. R., Sahrani, A., Leunk, D. K., Gui, P. Y., Voss, D., Ullah, K., Zoubek, M. J., Nippold, F., Aust, F., Vieira, F. F., Islam, C.-G., Zoubek, A. J., Shabani, S., Petter, J., Roos, I. B., Finnemann, A., Lob, A. B., Hoffstadt, M. F., Nak, J., de Ron, J., Derks, K., Huth, K., Terpstra, S., Bastelica, T., Matetovici, M., Ott, V. L., Zetea, A. S., Karnbach, K., Donzallaz, M. C., John, A., Moore, R. M., Assion, F., van Bork, R., Leidinger, T. E., Zhao, X., Motaghi, A. K., Pan, T., Armstrong, H., Peng, T., Bialas, M., Pang, J. Y.-C., Fu, B., Yang, S., Lin, X., Sleiffer, D., Bognar, M., Aczel, B., & Wagenmakers, E.-J. (2023). Fair coins tend to land on the same side they started: Evidence from 350,757 flips. \emph{arXiv}, 2310.04153, v2. \verb{https://arxiv.org/abs/2310.04153} } \references{ Diaconis, P., Holmes, S., & Montgomery, R. (2007). Dynamical bias in the coin toss. \emph{SIAM Review}, \bold{49}(2), 211--235. \verb{https://doi.org/10.1137/s0036144504446436} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bartos2023 dat \dontrun{ ### load metafor package library(metafor) ### compute proportions and the corresponding sampling variances dat <- escalc(measure="PR", xi=same, ni=flips, data=dat, slab=person) dat ### compute confidence intervals for the individual proportions (as in Table 1) summary(dat, digits=3)[c(1,6:8,13,14)] ### compute a confidence interval based on the column totals summary(escalc(measure="PR", xi=sum(dat$same), ni=sum(dat$flips)), digits=3) ### this is the same as meta-analyzing the proportions directly using an equal-effects ### model and also computing the sampling variances under the assumption that the true ### proportions are homogeneous rma(measure="PR", xi=same, ni=flips, vtype="AV", method="EE", data=dat, digits=3) ### fit a random-effects model res <- rma(yi, vi, data=dat) res ### profile likelihood confidence interval for tau^2 confint(res, type="PL") ### forest plot forest(res, refline=0.5, xlim=c(0.38,0.72), digits=c(3,2), efac=c(0,1)) ### funnel plot funnel(res, xlim=c(0.45,0.6), ylim=c(0,0.02)) ### fit a random-effects model excluding those with same-side proportions larger than 0.53 res <- rma(yi, vi, data=dat, subset=yi<=0.53) res confint(res, type="PL") ### fit a binomial-normal model res <- rma.glmm(measure="PLO", xi=same, ni=flips, data=dat) res predict(res, transf=plogis) ### conduct a meta-analysis for the proportions of heads (to examine heads-tails bias) dat <- escalc(measure="PR", xi=hdiff+hsame, ni=flips, data=dat) res <- rma(yi, vi, data=dat) res confint(res, type="PL") ### restructure the dataset for a bivariate meta-analysis of same-side and heads proportions dat <- dat.bartos2023 dat <- dat[rep(1:nrow(dat), each=2),] rownames(dat) <- NULL dat$outcome <- c("heads", "same") dat <- escalc(measure="PR", xi=hsame+hdiff, ni=flips, data=dat, include=outcome=="heads") dat <- escalc(measure="PR", xi=hsame+tsame, ni=flips, data=dat, include=outcome=="same") dat ### construct the 2x2 variance-covariance matrix of the proportions within persons dat$cov <- with(dat, (hsame/flips * (1-hsame/flips) - hsame/flips * tsame/flips - hsame/flips * hdiff/flips - hdiff/flips * tsame/flips) / flips) V <- lapply(split(dat, dat$person), \(x) matrix(c(x$vi[1], x$cov, x$vi[2]), nrow=2)) ### fit bivariate meta-analysis model res <- rma.mv(yi, V, mods = ~ 0 + outcome, random = ~ outcome | person, struct="UN", data=dat) res ### create plot with confidence ellipses ('ellipse' package must be installed) library(ellipse) plot(NA, xlim=c(0.45,0.62), ylim=c(0.45,0.62), bty="l", xlab="Pr(heads)", ylab="Pr(same)") abline(h=0.5, lty="dotted") abline(v=0.5, lty="dotted") # add confidence ellipses for persons invisible(tapply(dat, dat$person, \(x) { xy <- ellipse(matrix(c(x$vi[1],x$cov,x$vi[2]), nrow=2), centre=x$yi, level=0.95) lines(xy[,1],xy[,2], col="gray80") })) # add the points invisible(tapply(dat, dat$person, \(x) points(x$yi[1], x$yi[2], pch=21, bg="gray80", cex=1.5))) # add the 95\% PI ellipsis based on the model xy <- ellipse(res$G, centre=coef(res), level=0.95) lines(xy[,1],xy[,2], col="gray30", lwd=3, lty="dotted") # add the 95\% CI ellipsis based on the model xy <- ellipse(vcov(res), centre=coef(res), level=0.95) lines(xy[,1],xy[,2], col="gray30", lwd=3) # add the point for the pooled effects points(coef(res)[1], coef(res)[2], pch=21, bg="gray40", cex=2) } } \keyword{datasets} \concept{physics} \concept{human factors} \concept{proportions} \concept{multivariate models} \section{Concepts}{ physics, human factors, proportions, multivariate models } metadat/man/dat.dorn2007.Rd0000644000176200001440000000666614750466700015004 0ustar liggesusers\name{dat.dorn2007} \docType{data} \alias{dat.dorn2007} \title{Studies on Complementary and Alternative Medicine for Irritable Bowel Syndrome} \description{Results from 19 trials examining complementary and alternative medicine (CAM) for irritable bowel syndrome (IBS).} \usage{ dat.dorn2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab trial id number \cr \bold{study} \tab \code{character} \tab (first) author \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{country} \tab \code{character} \tab country where trial was conducted \cr \bold{ibs.crit} \tab \code{character} \tab IBS diagnostic criteria (Manning, Rome I, Rome II, or Other) \cr \bold{days} \tab \code{numeric} \tab number of treatment days \cr \bold{visits} \tab \code{numeric} \tab number of practitioner visits \cr \bold{jada} \tab \code{numeric} \tab Jadad score \cr \bold{x.a} \tab \code{numeric} \tab number of responders in the active treatment group \cr \bold{n.a} \tab \code{numeric} \tab number of participants in the active treatment group \cr \bold{x.p} \tab \code{numeric} \tab number of responders in the placebo group \cr \bold{n.p} \tab \code{numeric} \tab number of participants in the placebo group } } \details{ The dataset includes the results from 19 randomized clinical trials that examined the effectiveness of complementary and alternative medicine (CAM) for irritable bowel syndrome (IBS). } \note{ The data were extracted from Table I in Dorn et al. (2009). Comparing the funnel plot in Figure 1 with the one obtained below indicates that the data for study 5 (Davis et al., 2006) in the table were not the ones that were used in the actual analyses. } \source{ Dorn, S. D., Kaptchuk, T. J., Park, J. B., Nguyen, L. T., Canenguez, K., Nam, B. H., Woods, K. B., Conboy, L. A., Stason, W. B., & Lembo, A. J. (2007). A meta-analysis of the placebo response in complementary and alternative medicine trials of irritable bowel syndrome. \emph{Neurogastroenterology & Motility}, \bold{19}(8), 630--637. \verb{https://doi.org/10.1111/j.1365-2982.2007.00937.x} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.dorn2007 dat \dontrun{ ### load metafor package library(metafor) ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=x.a, n1i=n.a, ci=x.p, n2i=n.p, data=dat) ### random-effects model res <- rma(yi, vi, data=dat, digits=2, method="DL") res ### estimated average risk ratio predict(res, transf=exp) ### funnel plot with study 5 highlighted in red funnel(res, atransf=exp, at=log(c(0.1, 0.2, 0.5, 1, 2, 5, 10)), ylim=c(0,1), steps=6, las=1, col=ifelse(id == 5, "red", "black")) ### change log risk ratio for study 5 dat$yi[5] <- -0.44 ### results are now more in line with what is reported in the paper ### (although the CI in the paper is not wide enough) res <- rma(yi, vi, data=dat, digits=2, method="DL") predict(res, transf=exp) ### funnel plot with study 5 highlighted in red funnel(res, atransf=exp, at=log(c(0.1, 0.2, 0.5, 1, 2, 5, 10)), ylim=c(0,1), steps=6, las=1, col=ifelse(id == 5, "red", "black")) } } \keyword{datasets} \concept{medicine} \concept{alternative medicine} \concept{risk ratios} \section{Concepts}{ medicine, alternative medicine, risk ratios } metadat/man/dat.ursino2021.Rd0000644000176200001440000001414214750466700015341 0ustar liggesusers\name{dat.ursino2021} \docType{data} \alias{dat.ursino2021} \title{Sorafenib Toxicity Dataset} \description{13 studies investigating the occurrence of dose limiting toxicities (DLTs) at different doses of Sorafenib.} \usage{ dat.ursino2021 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{year} \tab \code{integer} \tab publication year \cr \bold{dose} \tab \code{numeric} \tab dose (\eqn{\mathrm{mg}}) \cr \bold{events} \tab \code{integer} \tab number of DLTs \cr \bold{total} \tab \code{integer} \tab number of patients exposed \cr } } \details{ Sorafenib (BAY 43-9006, Nexavar) is a kinase inhibitor that is used in the treatment of advanced renal cell carcinoma, hepatocellular carcinoma, and radioactive iodine resistant advanced thyroid carcinoma. Thirteen trials with published results, described in eleven manuscripts, were identified in a literature search. This dataset contains the doses investigated, the numbers of patients treated, and the number of dose-limiting toxicities (DLTs) observed. In general, each study investigated several doses according to some dose-escalation scheme. } \source{ Ursino, M., Roever, C., Zohar, S., & Friede T. (2021). Random-effects meta-analysis of phase I dose-finding studies using stochastic process priors. \emph{The Annals of Applied Statistics}, \bold{15}(1), 174--193. \verb{https://doi.org/10.1214/20-AOAS1390} Roever, C., Ursino, M., Friede, T., & Zohar, S. (2022). A straighforward meta-analysis approach for oncology phase I dose-finding studies. \emph{Statistics in Medicine}, \bold{41}(20), 3915--3940. \verb{https://doi.org/10.1002/sim.9484} } \references{ European Medicines Agency (EMA) (2022). Nexavar (sorafenib) EPAR summary. \verb{https://www.ema.europa.eu/en/medicines/human/EPAR/nexavar} Awada, A., Hendlisz, A., Gil, T., \emph{et al.} (2005). Phase I safety and pharmacokinetics of BAY 43-9006 administered for 21 days on / 7 days off in patients with advanced, refractory solid tumours. \emph{British Journal of Cancer}, \bold{92}(10), 1855. \verb{https://doi.org/10.1038/sj.bjc.6602584} Clark, J. W., Eder, J. P., Ryan, D., Lathia, C., & Lenz, H.-J. (2005). Safety and pharmacokinetics of the dual action Raf kinase and vascular endothelial growth factor receptor inhibitor, BAY 43-9006, in patients with advanced, refractory solid tumors. \emph{Clinical Cancer Research}, \bold{11}(15), 5472--5480. \verb{https://doi.org/10.1158/1078-0432.CCR-04-2658} Moore, M., Hirte, H. W., Siu, L., \emph{et al.} (2005). Phase I study to determine the safety and pharmacokinetics of the novel Raf kinase and VEGFR inhibitor BAY 43-9006, administered for 28 days on / 7 days off in patients with advanced, refractory solid tumors. \emph{Annals of Oncology}, \bold{16}(10), 1688--1694. \verb{https://doi.org/10.1093/annonc/mdi310} Strumberg, D., Richly, H., Hilger, R. A., \emph{et al.} (2005). Phase I clinical and pharmacokinetic study of the novel Raf kinase and vascular endothelial growth factor receptor inhibitor BAY 43-9006 in patients with advanced refractory solid tumors. \emph{Journal of Clinical Oncology}, \bold{23}(5), 965--972. \verb{https://doi.org/10.1200/JCO.2005.06.124} Furuse, J., Ishii, H., Nakachi, K., Suzuki, E., Shimizu, S., & Nakajima, K. (2008). Phase I study of sorafenib in Japanese patients with hepatocellular carcinoma. \emph{Cancer Science}, \bold{99}(1), 159--165. \verb{https://doi.org/10.1111/j.1349-7006.2007.00648.x} Minami, H., Kawada, K., Ebi, H., \emph{et al.} (2008). Phase I and pharmacokinetic study of sorafenib, an oral multikinase inhibitor, in Japanese patients with advanced refractory solid tumors. \emph{Cancer Science}, \bold{99}(7), 1492--1498. \verb{https://doi.org/10.1111/j.1349-7006.2008.00837.x} Miller, A. A., Murry, D. J., Owzar, K., \emph{et al.} (2009). Phase I and pharmacokinetic study of sorafenib in patients with hepatic or renal dysfunction: CALGB 60301. \emph{Journal of Clinical Oncology}, \bold{27}(11), 1800. \verb{https://doi.org/10.1200/JCO.2008.20.0931} Crump, M., Hedley, D., Kamel-Reid, S., \emph{et al.} (2010). A randomized phase I clinical and biologic study of two schedules of sorafenib in patients with myelodysplastic syndrome or acute myeloid leukemia: A NCIC (National Cancer Institute of Canada) Clinical Trials Group Study. \emph{Leukemia and Lymphoma}, \bold{51}(2), 252--260. \verb{https://doi.org/10.3109/10428190903585286} Borthakur, G., Kantarjian, H., Ravandi, F., \emph{et al.} (2011). Phase I study of sorafenib in patients with refractory or relapsed acute Leukemias. \emph{Haematologica}, \bold{96}(1), 62--68. \verb{https://doi.org/10.3324/haematol.2010.030452} Nabors, L. B., Supko, J. G., Rosenfeld, M., \emph{et al.} (2011). Phase I trial of sorafenib in patients with recurrent or progressive malignant glioma. \emph{Neuro-Oncology}, \bold{13}(12), 1324--1330. \verb{https://doi.org/10.1093/neuonc/nor145} Chen, Y.-B., Li, S., Lane, A. A., \emph{et al.} (2014). Phase I trial of maintenance sorafenib after allogeneic hematopoietic stem cell transplantation for FMS-like tyrosine kinase 3 internal tandem duplication acute myeloid leukemia. \emph{Biology of Blood and Marrow Transplantation}, \bold{20}(12), 2042--2048. \verb{https://doi.org/10.1016/j.bbmt.2014.09.007} } \seealso{ \code{\link{dat.roever2022}} } \author{ Christian Roever, \email{christian.roever@med.uni-goettingen.de} } \examples{ # show (some) data head(dat.ursino2021, n=15) \dontrun{ # illustrate data plot(NA, xlim=range(dat.ursino2021$dose), ylim=0:1, xlab="dose (mg)", ylab="proportion", main="dat.ursino2021 (Sorafenib data)") studylab <- unique(dat.ursino2021$study) colvec <- rainbow(length(studylab)) for (i in 1:length(studylab)) { idx <- (dat.ursino2021$study == studylab[i]) lines(dat.ursino2021[idx,"dose"], dat.ursino2021[idx,"events"] / dat.ursino2021[idx,"total"], col=colvec[i], type="b") } legend("topleft", studylab, col=colvec, pch=15) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{dose-response models} \section{Concepts}{ medicine, oncology, dose-response models } metadat/man/dat.crede2010.Rd0000644000176200001440000001003714750466700015101 0ustar liggesusers\name{dat.crede2010} \docType{data} \alias{dat.crede2010} \title{Studies on the Relationship between Class Attendance and Grades in College Students} \description{Results from 68 studies on the relationship between class attendence and class performance and/or grade point average in college students.} \usage{ dat.crede2010 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{studyid} \tab \code{numeric} \tab study number \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{source} \tab \code{character} \tab study source (journal, dissertation, other) \cr \bold{sampleid} \tab \code{numeric} \tab sample within study number \cr \bold{criterion} \tab \code{character} \tab criterion variable (grade, gpa) \cr \bold{class} \tab \code{character} \tab class type (science, nonscience) \cr \bold{ni} \tab \code{numeric} \tab sample size \cr \bold{ri} \tab \code{numeric} \tab observed correlation } } \details{ The 68 studies included in this dataset provide information about the relationship between class attendance of college students and their performance (i.e., grade) in the class and/or their overall grade point average. Some studies included multiple samples and hence the dataset actually contains 97 correlation coefficients. The dataset was obtained via personal communication. Note that this dataset differs just slightly from the one used by Credé et al. (2010). } \source{ Personal communication. } \references{ Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. \emph{Review of Educational Research}, \bold{80}(2), 272--295. \verb{https://doi.org/10.3102/0034654310362998} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.crede2010 head(dat, 18) \dontrun{ ### load metafor package library(metafor) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat) ############################################################################ ### meta-analysis for the relationship between attendance and grades res <- rma(yi, vi, data=dat, subset=criterion=="grade") res ### estimated average correlation with 95\% CI/PI predict(res, transf=transf.ztor, digits=2) ### examine if relationship between attendance and grades differs for nonscience/science classes res <- rma(yi, vi, mods = ~ class, data=dat, subset=criterion=="grade") res ### estimated average correlations for nonscience and science classes predict(res, newmods=c(0,1), transf=transf.ztor, digits=2) ### examine if relationship between attendance and grades has changed over time res <- rma(yi, vi, mods = ~ year, data=dat, subset=criterion=="grade") res ############################################################################ ### meta-analysis for the relationship between attendance and GPA res <- rma(yi, vi, data=dat, subset=criterion=="gpa") res ### estimated average correlation with 95\% CI/PI predict(res, transf=transf.ztor, digits=2) ### examine if relationship between attendance and GPA has changed over time res <- rma(yi, vi, mods = ~ year, data=dat, subset=criterion=="gpa") res ############################################################################ ### use a multilevel model to examine the relationship between attendance and grades res <- rma.mv(yi, vi, random = ~ 1 | studyid/sampleid, data=dat, subset=criterion=="grade") res predict(res, transf=transf.ztor, digits=2) ### use a multilevel model to examine the relationship between attendance and gpa res <- rma.mv(yi, vi, random = ~ 1 | studyid/sampleid, data=dat, subset=criterion=="gpa") res predict(res, transf=transf.ztor, digits=2) } } \keyword{datasets} \concept{education} \concept{correlation coefficients} \concept{multilevel models} \section{Concepts}{ education, correlation coefficients, multilevel models } metadat/man/dat.tannersmith2016.Rd0000644000176200001440000001103614750466700016361 0ustar liggesusers\name{dat.tannersmith2016} \docType{data} \alias{dat.tannersmith2016} \title{Studies on the Relationship between School Motivation and Criminal Behavior} \description{Results from 17 studies on the correlation between school motivation/attitudes and subsequent delinquent/criminal behavior.} \usage{ dat.tannersmith2016 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{studyid} \tab \code{numeric} \tab study identifier \cr \bold{yi} \tab \code{numeric} \tab r-to-z transformed correlation coefficient \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr \bold{sei} \tab \code{numeric} \tab corresponding standard error \cr \bold{aget1} \tab \code{numeric} \tab age at which the school motivation/attitudes were assessed \cr \bold{aget2} \tab \code{numeric} \tab age at which the delinquent/criminal behavior was assessed \cr \bold{propmale} \tab \code{numeric} \tab proportion of male participants in the sample \cr \bold{sexmix} \tab \code{character} \tab whether the sample consisted only of males, only of females, or a mix } } \details{ The dataset includes 113 r-to-z transformed correlation coefficients from 17 prospective longitudinal studies that examined the relationship between school motivation/attitudes and subsequent delinquent/criminal behavior. Multiple coefficients could be extracted from the studies \dQuote{given the numerous ways in which school motivation/attitudes variables could be operationalized (e.g., academic aspirations, academic self-efficacy) as well as the numerous ways in which crime/delinquency could be operationalized (e.g., property crime, violent crime)} (Tanner-Smith et al., 2016). Since information to compute the covariance between multiple coefficients within studies is not available, Tanner-Smith et al. (2016) illustrate the use of cluster-robust inference methods for the analysis of this dataset. Note that this dataset is only meant to be used for pedagogical and demonstration purposes and does not constitute a proper review or synthesis of the complete and current research evidence on the given topic. } \source{ Tanner-Smith, E. E., Tipton, E. & Polanin, J. R. (2016). Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R. \emph{Journal of Developmental and Life-Course Criminology}, \bold{2}(1), 85--112. \verb{https://doi.org/10.1007/s40865-016-0026-5} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.tannersmith2016 head(dat) \dontrun{ ### load metafor package library(metafor) ### compute mean age variables within studies dat$aget1 <- ave(dat$aget1, dat$studyid) dat$aget2 <- ave(dat$aget2, dat$studyid) ### construct an effect size identifier variable dat$esid <- 1:nrow(dat) ### construct an approximate var-cov matrix assuming a correlation of 0.8 ### for multiple coefficients arising from the same study V <- vcalc(vi, cluster=studyid, obs=esid, rho=0.8, data=dat) ### fit a multivariate random-effects model using the approximate var-cov matrix V res <- rma.mv(yi, V, random = ~ esid | studyid, data=dat) res ### use cluster-robust inference methods robust(res, cluster=studyid, clubSandwich=TRUE) ### note: the results obtained above and below are slightly different compared ### to those given by Tanner-Smith et al. (2016) since the approach illustrated ### here makes use a multivariate random-effects model for the 'working model' ### before applying the cluster-robust inference methods, while the results given ### in the paper are based on a somewhat simpler working model ### examine the main effects of the age variables res <- rma.mv(yi, V, mods = ~ aget1 + aget2, random = ~ 1 | studyid/esid, data=dat) robust(res, cluster=studyid, clubSandwich=TRUE) ### also examine their interaction res <- rma.mv(yi, V, mods = ~ aget1 * aget2, random = ~ 1 | studyid/esid, data=dat) robust(res, cluster=studyid, clubSandwich=TRUE) ### add the sexmix factor to the model res <- rma.mv(yi, V, mods = ~ aget1 * aget2 + sexmix, random = ~ 1 | studyid/esid, data=dat) robust(res, cluster=studyid, clubSandwich=TRUE) } } \keyword{datasets} \concept{psychology} \concept{criminology} \concept{correlation coefficients} \concept{multilevel models} \concept{cluster-robust inference} \concept{meta-regression} \section{Concepts}{ psychology, criminology, correlation coefficients, multilevel models, cluster-robust inference, meta-regression } metadat/man/dat.hine1989.Rd0000644000176200001440000000521214750466700014771 0ustar liggesusers\name{dat.hine1989} \docType{data} \alias{dat.hine1989} \title{Studies on Prophylactic Use of Lidocaine After a Heart Attack} \description{Results from 6 studies evaluating mortality from prophylactic use of lidocaine in acute myocardial infarction.} \usage{ dat.hine1989 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{source} \tab \code{character} \tab source of data \cr \bold{n1i} \tab \code{numeric} \tab number of patients in lidocaine group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in control group \cr \bold{ai} \tab \code{numeric} \tab number of deaths in lidocaine group \cr \bold{ci} \tab \code{numeric} \tab number of deaths in control group } } \details{ Hine et al. (1989) conducted a meta-analysis of death rates in randomized controlled trials in which prophylactic lidocaine was administered to patients with confirmed or suspected acute myocardial infarction. The dataset describes the mortality at the end of the assigned treatment period for control and intravenous lidocaine treatment groups for six studies. The question of interest is whether there is a detrimental effect of lidocaine. Because the studies were conducted to compare rates of arrhythmias following a heart attack, the studies, taken individually, are too small to detect important differences in mortality rates. The data in this dataset were obtained from Table I in Normand (1999, p. 322). } \source{ Normand, S. T. (1999). Meta-analysis: Formulating, evaluating, combining, and reporting. \emph{Statistics in Medicine}, \bold{18}(3), 321--359. \verb{https://doi.org/10.1002/(sici)1097-0258(19990215)18:3<321::aid-sim28>3.0.co;2-p} } \references{ Hine, L. K., Laird, N., Hewitt, P., & Chalmers, T. C. (1989). Meta-analytic evidence against prophylactic use of lidocaine in acute myocardial infarction. \emph{Archives of Internal Medicine}, \bold{149}(12), 2694--2698. \verb{https://doi.org/10.1001/archinte.1989.00390120056011} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hine1989 dat \dontrun{ ### load metafor package library(metafor) ### calculate risk differences and corresponding sampling variances dat <- escalc(measure="RD", n1i=n1i, n2i=n2i, ai=ai, ci=ci, data=dat) dat ### meta-analysis of risk differences using a random-effects model res <- rma(yi, vi, data=dat) res } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{risk differences} \section{Concepts}{ medicine, cardiology, risk differences } metadat/man/dat.roever2022.Rd0000644000176200001440000001451114750466700015325 0ustar liggesusers\name{dat.roever2022} \docType{data} \alias{dat.roever2022} \title{Irinotecan / S-1 Toxicity Dataset} \description{12 studies investigating the occurrence of dose limiting toxicities (DLTs) at different doses of a combination therapy of Irinotecan and S-1.} \usage{ dat.roever2022 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{year} \tab \code{integer} \tab publication year \cr \bold{dose} \tab \code{numeric} \tab dose (\eqn{\mathrm{mg} / \mathrm{m}^2}) \cr \bold{events} \tab \code{integer} \tab number of DLTs \cr \bold{total} \tab \code{integer} \tab number of patients exposed \cr } } \details{ A combination therapy of Irinotecan (a topoisomerase 1 inhibitor) and S-1 (a combination of three pharmacological compounds, namely, tegafur, gimeracil, and oteracil potassium) was tested in advanced colorectal and gastric cancer. This dataset contains data from twelve studies investigating this therapy in a Japanese population; it contains the doses investigated, the numbers of patients treated, and the number of dose-limiting toxicities (DLTs) observed. In general, each study investigated several doses according to some dose-escalation scheme. } \source{ Ursino, M., Roever, C., Zohar, S., & Friede T. (2021). Random-effects meta-analysis of phase I dose-finding studies using stochastic process priors. \emph{The Annals of Applied Statistics}, \bold{15}(1), 174--193. \verb{https://doi.org/10.1214/20-AOAS1390} Roever, C., Ursino, M., Friede, T., & Zohar, S. (2022). A straighforward meta-analysis approach for oncology phase I dose-finding studies. \emph{Statistics in Medicine}, \bold{41}(20), 3915--3940. \verb{https://doi.org/10.1002/sim.9484} } \references{ European Medicines Agency (EMA) (2021). Onivyde pegylated liposomal (irinotecan hydrochloride trihydrate) EPAR summary. \verb{https://www.ema.europa.eu/en/medicines/human/EPAR/onivyde-pegylated-liposomal} European Medicines Agency (EMA) (2022). Teysuno (tegafur/gimeracil/oteracil) EPAR summary. \verb{https://www.ema.europa.eu/en/medicines/human/EPAR/teysuno} Yamada, Y., Yasui, H., Goto, A., \emph{et al.} (2003). Phase I study of irinotecan and S-1 combination therapy in patients with metastatic gastric cancer. \emph{International Journal of Clinical Oncology}, \bold{8}(6), 374--380. \verb{https://doi.org/10.1007/s10147-003-0359-z} Takiuchi, H., Narahara, H., Tsujinaka, T., \emph{et al.} (2005). Phase I study of S-1 combined with irinotecan (CPT-11) in patients with advanced gastric cancer (OGSG 0002). \emph{Japanese Journal of Clinical Oncology}, \bold{35}(9), 520--525. \verb{https://doi.org/10.1093/jjco/hyi148} Inokuchi, M., Yamashita, T., Yamada, H., \emph{et al.} (2006). Phase I/II study of S-1 combined with irinotecan for metastatic advanced gastric cancer. \emph{British Journal of Cancer}, \bold{94}(8), 11130. \verb{https://doi.org/10.1038/sj.bjc.6603072} Nakafusa, Y., Tanaka, M., Ohtsuka, T., \emph{et al.} (2008). Phase I/II study of combination therapy with S-1 and CPT-11 for metastatic colorectal cancer. \emph{Molecular Medicine Reports}, \bold{1}(6), 925--930. \verb{https://doi.org/10.3892/mmr_00000051} Ishimoto, O., Ishida, T., Honda, Y., Munakata, M., & Sugawara, S. (2009). Phase I study of daily S-1 combined with weekly irinotecan in patients with advanced non-small cell lung cancer. \emph{International Journal of Clinical Oncology}, \bold{14}(1), 43--47. \verb{https://doi.org/10.1007/s10147-008-0796-9} Ogata, Y., Sasatomi, T., Akagi, Y., Ishibashi, N., Mori, S., & Shirouzu, K. (2009). Dosage escalation study of S-1 and irinotecan in metronomic chemotherapy against advanced colorectal cancer. \emph{The Kurume Medical Journal}, \bold{56}(1+2), 1--7. \verb{https://doi.org/10.2739/kurumemedj.56.1} Shiozawa, M., Sugano, N., Tsuchida, K., Morinaga, S., Akaike, M., & Sugimasa, Y. (2009). A phase I study of combination therapy with S-1 and irinotecan (CPT-11) in patients with advanced colorectal cancer. \emph{Journal of Cancer Research and Clinical Oncology}, \bold{135}(3), 365--370. \verb{https://doi.org/10.1007/s00432-008-0480-5} Yoshioka, T., Kato, S., Gamoh, M., \emph{et al.} (2009). Phase I/II study of sequential therapy with irinotecan and S-1 for metastatic colorectal cancer. \emph{British Journal of Cancer}, \bold{101}, 1972--1977. \verb{https://doi.org/10.1038/sj.bjc.6605432} Komatsu, Y., Yuki, S., Fuse, N., \emph{et al.} (2010). Phase 1/2 clinical study of irinotecan and oral S-1 (IRIS) in patients with advanced gastric cancer. \emph{Advances in Therapy}, \bold{27}(7), 483--492. \verb{https://doi.org/10.1007/s12325-010-0037-2} Kusaba, H., Esaki, T., Futami, K., \emph{et al.} (2010). Phase I/II study of a 3-week cycle of irinotecan and S-1 in patients with advanced colorectal cancer. \emph{Cancer Science}, \bold{101}(12), 2591--2595. \verb{https://doi.org/10.1111/j.1349-7006.2010.01728.x} Yoda, S., Soejima, K., Yasuda, H., \emph{et al.} (2011). A phase I study of S-1 and irinotecan combination therapy in previously treated advanced non-small cell lung cancer patients. \emph{Cancer Chemotherapy and Pharmacology}, \bold{67}(3), 717--722. \verb{https://doi.org/10.1007/s00280-010-1539-y} Goya, H., Kuraishi, H., Koyama, S., \emph{et al.} (2012). Phase I/II study of S-1 combined with biweekly irinotecan chemotherapy in previously treated advanced non-small cell lung cancer. \emph{Cancer Chemotherapy and Pharmacology}, \bold{70}(5), 691--697. \verb{https://doi.org/10.1007/s00280-012-1957-0} } \seealso{ \code{\link{dat.ursino2021}} } \author{ Christian Roever, \email{christian.roever@med.uni-goettingen.de} } \examples{ # show (some) data head(dat.roever2022, n=10) \dontrun{ # illustrate data plot(NA, xlim=range(dat.roever2022$dose), ylim=0:1, xlab="dose (mg / m²)", ylab="proportion", main="dat.roever2022 (Irinotecan / S-1 data)") studylab <- unique(dat.roever2022$study) colvec <- rainbow(length(studylab)) for (i in 1:length(studylab)) { idx <- (dat.roever2022$study == studylab[i]) lines(dat.roever2022[idx,"dose"], dat.roever2022[idx,"events"] / dat.roever2022[idx,"total"], col=colvec[i], type="b") } legend("topleft", studylab, col=colvec, pch=15) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{dose-response models} \section{Concepts}{ medicine, oncology, dose-response models } metadat/man/dat.riley2003.Rd0000644000176200001440000000731514750466700015152 0ustar liggesusers\name{dat.riley2003} \docType{data} \alias{dat.riley2003} \title{Studies on MYC-N as a Prognostic Marker for Neuroblastoma} \description{Results from 81 studies examining overall and disease-free survival in neuroblastoma patients with amplified versus normal MYC-N protein levels.} \usage{ dat.riley2003 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{yi} \tab \code{numeric} \tab log hazard ratio of the outcome in those with amplified versus normal MYC-N protein levels \cr \bold{vi} \tab \code{numeric} \tab sampling variance of the log hazard ratio \cr \bold{sei} \tab \code{numeric} \tab standard error of the log hazard ratio \cr \bold{outcome} \tab \code{character} \tab outcome (OS = overall survival; DFS = disease-free survival) } } \details{ The meta-analysis by Riley et al. (2003) examined a variety of prognostic markers for overall and disease-free survival in patients with neuroblastoma. One of the markers examined was amplified levels of the MYC-N protein, with is associated with poorer outcomes. The dataset given here was extracted from Riley (2011) and has been used in several other publications (e.g., Riley et al., 2004, 2007). The dataset provides the (log) hazard ratios (and corresponding standard errors) with respect to these two outcomes in 81 studies, with positive values indicating a greater risk of death (for OS) or disease recurrence/death (for DFS) for patients with high MYC-N levels compared to those with normal/low levels. Note that information on both outcomes could only be extracted from 17 studies (39 studies only provided sufficient information to extract the OS estimate, while 25 studies only allowed for extraction of the DFS estimate). } \source{ Riley, R. D., Sutton, A. J., Abrams, K. R., & Lambert, P. C. (2004). Sensitivity analyses allowed more appropriate and reliable meta-analysis conclusions for multiple outcomes when missing data was present. \emph{Journal of Clinical Epidemiology}, \bold{57}(9), 911--924. \verb{https://doi.org/10.1016/j.jclinepi.2004.01.018} Riley, R. D., Abrams, K. R., Lambert, P. C., Sutton, A. J., & Thompson, J. R. (2007). An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. \emph{Statistics in Medicine}, \bold{26}(1), 78--97. \verb{https://doi.org/10.1002/sim.2524} Riley, R. D. (2011). Erratum: An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. \emph{Statistics in Medicine}, \bold{30}(4), 400. \verb{https://doi.org/10.1002/sim.4100} } \references{ Riley, R. D., Burchill, S. A., Abrams, K. R., Heney, D., Lambert, P. C., Jones, D. R., Sutton, A. J., Young, B., Wailoo, A. J., & Lewis, I. J. (2003). A systematic review and evaluation of the use of tumour markers in paediatric oncology: Ewing's sarcoma and neuroblastoma. \emph{Health Technology Assessment}, \bold{7}(5), 1--162. \verb{https://doi.org/10.3310/hta7050} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.riley2003 dat \dontrun{ ### load metafor package library(metafor) ### random-effects model analysis for outcome DFS res <- rma(yi, sei=sei, data=dat, subset=(outcome == "DFS"), method="DL") res predict(res, transf=exp, digits=2) ### random-effects model analysis for outcome OS res <- rma(yi, sei=sei, data=dat, subset=(outcome == "OS"), method="DL") res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{hazard ratios} \section{Concepts}{ medicine, oncology, hazard ratios } metadat/man/dat.ishak2007.Rd0000644000176200001440000001116014750466700015122 0ustar liggesusers\name{dat.ishak2007} \docType{data} \alias{dat.ishak2007} \title{Studies on Deep-Brain Stimulation in Patients with Parkinson's disease} \description{Results from 46 studies examining the effects of deep-brain stimulation on motor skills of patients with Parkinson's disease.} \usage{ dat.ishak2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab (first) author and year \cr \bold{y1i} \tab \code{numeric} \tab observed mean difference at 3 months \cr \bold{v1i} \tab \code{numeric} \tab sampling variance of the mean difference at 3 months \cr \bold{y2i} \tab \code{numeric} \tab observed mean difference at 6 months \cr \bold{v2i} \tab \code{numeric} \tab sampling variance of the mean difference at 6 months \cr \bold{y3i} \tab \code{numeric} \tab observed mean difference at 12 months \cr \bold{v3i} \tab \code{numeric} \tab sampling variance of the mean difference at 12 months \cr \bold{y4i} \tab \code{numeric} \tab observed mean difference at the long-term follow-up \cr \bold{v4i} \tab \code{numeric} \tab sampling variance of the mean difference at the long-term follow-up \cr \bold{mdur} \tab \code{numeric} \tab mean disease duration (in years) \cr \bold{mbase} \tab \code{numeric} \tab mean baseline UPDRS score } } \details{ Deep-brain stimulation (DBS), which is delivered through thin surgically implanted wires in specific areas of the brain and controlled by the patient, is meant to provide relief of the debilitating symptoms of Parkinson's disease. The dataset includes the results from 46 studies examining the effects of DBS of the subthalamic nucleus on motor functioning, measured with the Unified Parkinson's Disease Rating Scale (UPDRS). The effect size measure for this meta-analysis was the mean difference of the scores while the stimulator is active and the baseline scores (before implantation of the stimulator). Since lower scores on the UPDRS indicate better functioning, negative numbers indicate improvements in motor skills. Effects were generally measured at 3, 6, and 12 months after implantation of the stimulator, with some studies also including a further long-term follow-up. However, the number of measurements differed between studies - hence the missing data on some of the measurement occasions. Since the same patients were followed over time within a study, effect size estimates from multiple measurement occasions are likely to be correlated. A multivariate model accounting for the correlation in the effects can be used to meta-analyze these data. A difficulty with this approach is the lack of information about the correlation of the measurements over time in the individual studies. The approach taken by Ishak et al. (2007) was to assume an autoregressive (AR1) structure for the estimates within the individual studies. In addition, the correlation in the true effects was modeled, again using an autoregressive structure. } \source{ Ishak, K. J., Platt, R. W., Joseph, L., Hanley, J. A., & Caro, J. J. (2007). Meta-analysis of longitudinal studies. \emph{Clinical Trials}, \bold{4}(5), 525--539. \verb{https://doi.org/10.1177/1740774507083567} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.ishak2007 head(dat, 5) \dontrun{ ### load metafor package library(metafor) ### create long format dataset dat <- reshape(dat, direction="long", idvar="study", v.names=c("yi","vi"), varying=list(c(2,4,6,8), c(3,5,7,9))) dat <- dat[order(study, time),] ### remove missing measurement occasions from dat.long dat <- dat[!is.na(yi),] rownames(dat) <- NULL head(dat, 8) ### construct the full (block diagonal) V matrix with an AR(1) structure ### assuming an autocorrelation of 0.97 as estimated by Ishak et al. (2007) V <- vcalc(vi, cluster=study, time1=time, phi=0.97, data=dat) ### plot data with(dat, interaction.plot(time, study, yi, type="b", pch=19, lty="solid", xaxt="n", legend=FALSE, xlab="Time Point", ylab="Mean Difference", bty="l")) axis(side=1, at=1:4, lab=c("1 (3 months)", "2 (6 months)", "3 (12 months)", "4 (12+ months)")) ### multivariate model with heteroscedastic AR(1) structure for the true effects res <- rma.mv(yi, V, mods = ~ 0 + factor(time), random = ~ time | study, struct = "HAR", data = dat) print(res, digits=2) } } \keyword{datasets} \concept{medicine} \concept{raw mean differences} \concept{longitudinal models} \section{Concepts}{ medicine, raw mean differences, longitudinal models } metadat/man/dat.dogliotti2014.Rd0000644000176200001440000000620414750466700016022 0ustar liggesusers\name{dat.dogliotti2014} \docType{data} \alias{dat.dogliotti2014} \title{Studies on Antithrombotic Treatments to Prevent Strokes} \description{Results from 20 trials examining the effectiveness of antithrombotic treatments to prevent strokes in patients with non-valvular atrial fibrillation.} \usage{ dat.dogliotti2014 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{id} \tab \code{numeric} \tab study ID \cr \bold{treatment} \tab \code{character} \tab treatment \cr \bold{stroke} \tab \code{numeric} \tab number of strokes \cr \bold{total} \tab \code{numeric} \tab number of individuals } } \details{ This dataset comes from a systematic review aiming to estimate the effects of eight antithrombotic treatments including placebo in reducing the incidence of major thrombotic events in patients with non-valvular atrial fibrillation (Dogliotti et al., 2014). The review included 20 studies with 79,808 participants, four studies are three-arm studies. The primary outcome is stroke reduction (yes / no). } \source{ Dogliotti, A., Paolasso, E., & Giugliano, R. P. (2014). Current and new oral antithrombotics in non-valvular atrial fibrillation: A network meta-analysis of 79808 patients. \emph{Heart}, \bold{100}(5), 396--405. \verb{https://doi.org/10.1136/heartjnl-2013-304347} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first 7 rows / 3 studies of the dataset head(dat.dogliotti2014, 7) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Change appearance of confidence intervals cilayout("(", "-") ### Transform data from long arm-based format to contrast-based ### format. Argument 'sm' has to be used for odds ratio as summary ### measure; by default the risk ratio is used in the metabin function ### called internally. pw <- pairwise(treat = treatment, n = total, event = stroke, studlab = study, data = dat.dogliotti2014, sm = "OR") ### Print log odds ratios (TE) and standard errors (seTE) head(pw, 5)[, 1:5] ### Conduct network meta-analysis (NMA) with placebo as reference net <- netmeta(pw, ref = "plac") ### Details on excluded study selvars <- c("studlab", "event1", "n1", "event2", "n2") subset(pw, studlab == "WASPO, 2007")[, selvars] ### Show network graph netgraph(net, seq = "optimal", number = TRUE) ### Conduct Mantel-Haenszel NMA net.mh <- netmetabin(pw, ref = "plac") ### Compare results of inverse variance and Mantel-Haenszel NMA nb <- netbind(net, net.mh, random = FALSE, name = c("Inverse variance", "Mantel-Haenszel")) forest(nb, xlim = c(0.15, 2), at = c(0.2, 0.5, 1, 2)) ### Print and plot results for inverse variance NMA net forest(net) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{network meta-analysis} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, odds ratios, network meta-analysis, Mantel-Haenszel method } metadat/man/dat.egger2001.Rd0000644000176200001440000001154014750466700015110 0ustar liggesusers\name{dat.egger2001} \docType{data} \alias{dat.egger2001} \title{Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction} \description{Results from 16 trials examining the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction.} \usage{ dat.egger2001 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab trial id number \cr \bold{study} \tab \code{character} \tab first author or trial name \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ai} \tab \code{numeric} \tab number of deaths in the magnesium group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the magnesium group \cr \bold{ci} \tab \code{numeric} \tab number of deaths in the control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the control group } } \details{ The dataset includes the results from 16 randomized clinical trials that examined the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction. Studies 1-7 were included in the meta-analyses by Teo et al. (1991) and Horner (1992) and were combined with the results from the LIMIT-2 trial (Woods et al., 1992) in Yusuf et al. (1993), suggesting that magnesium is an effective treatment for reducing mortality. However, the results from the ISIS-4 mega trial (ISIS-4 Collaborative Group, 1995) indicated no reduction in mortality with magnesium treatment. Publication bias has been suggested as one possible explanation for the conflicting findings (Egger & Davey Smith, 1995). The present dataset includes some additional trials and are based on Table 18.2 from Egger, Davey Smith, and Altman (2001). } \source{ Egger, M., Davey Smith, G., & Altman, D. G. (Eds.) (2001). \emph{Systematic reviews in health care: Meta-analysis in context} (2nd ed.). London: BMJ Books. } \references{ Egger, M., & Davey Smith, G. (1995). Misleading meta-analysis: Lessons from \dQuote{an effective, safe, simple} intervention that wasn't. \emph{British Medical Journal}, \bold{310}(6982), 752--754. \verb{https://doi.org/10.1136/bmj.310.6982.752} Horner, S. M. (1992). Efficacy of intravenous magnesium in acute myocardial infarction in reducing arrhythmias and mortality: Meta-analysis of magnesium in acute myocardial infarction. \emph{Circulation}, \bold{86}(3), 774--779. \verb{https://doi.org/10.1161/01.cir.86.3.774} ISIS-4 Collaborative Group (1995). ISIS-4: A randomised factorial trial assessing early oral captopril, oral mononitrate, and intravenous magnesium sulphate in 58,050 patients with suspected acute myocardial infarction. \emph{Lancet}, \bold{345}(8951), 669--685. \verb{https://doi.org/10.1016/S0140-6736(95)90865-X} Teo, K. K., Yusuf, S., Collins, R., Held, P. H., & Peto, R. (1991). Effects of intravenous magnesium in suspected acute myocardial infarction: Overview of randomised trials. \emph{British Medical Journal}, \bold{303}(6816), 1499--1503. \verb{https://doi.org/10.1136/bmj.303.6816.1499} Woods, K. L., Fletcher, S., Roffe, C., & Haider, Y. (1992). Intravenous magnesium sulphate in suspected acute myocardial infarction: Results of the second Leicester Intravenous Magnesium Intervention Trial (LIMIT-2). \emph{Lancet}, \bold{339}(8809), 1553--1558. \verb{https://doi.org/10.1016/0140-6736(92)91828-v} Yusuf, S., Teo, K., & Woods, K. (1993). Intravenous magnesium in acute myocardial infarction: An effective, safe, simple, and inexpensive treatment. \emph{Circulation}, \bold{87}(6), 2043--2046. \verb{https://doi.org/10.1161/01.cir.87.6.2043} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \seealso{ \code{\link{dat.li2007}} } \examples{ ### copy data into 'dat' and examine data dat <- dat.egger2001 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of trials 1-7 using Peto's method (as in Teo et al., 1991) res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=1:7) print(res, digits=2) ### meta-analysis of trials 1-7 and LIMIT-2 (as in Yusuf et al., 1993) res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=c(1:7,14)) print(res, digits=2) ### meta-analysis of all trials except ISIS-4 res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=-16) print(res, digits=2) predict(res, transf=exp, digits=2) ### meta-analysis of all trials including ISIS-4 res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat) print(res, digits=2) predict(res, transf=exp, digits=2) ### contour-enhanced funnel plot centered at 0 funnel(res, refline=0, level=c(90, 95, 99), shade=c("white", "gray", "darkgray")) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{Peto's method} \concept{publication bias} \section{Concepts}{ medicine, cardiology, Peto's method, publication bias } metadat/man/dat.hartmannboyce2018.Rd0000644000176200001440000000521414750466700016662 0ustar liggesusers\name{dat.hartmannboyce2018} \docType{data} \alias{dat.hartmannboyce2018} \title{Studies on the Effectiveness of Nicotine Replacement Therapy for Smoking Cessation} \description{Results from 133 studies examining the effectiveness of nicotine replacement therapy (NRT) for smoking cessation at 6+ months of follow-up.} \usage{ dat.hartmannboyce2018 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study identifier \cr \bold{x.nrt} \tab \code{numeric} \tab number of participants in the NRT group who were abstinent at the follow-up \cr \bold{n.nrt} \tab \code{numeric} \tab number of participants in the NRT group \cr \bold{x.ctrl} \tab \code{numeric} \tab number of participants in the control group who were abstinent at the follow-up \cr \bold{n.ctrl} \tab \code{numeric} \tab number of participants in the control group \cr \bold{treatment} \tab \code{character} \tab type of NRT provided in the treatment group } } \details{ The dataset includes the results from 133 studies examining the effectiveness of nicotine replacement therapy (NRT) for smoking cessation. The results given in this dataset pertain to abstinence at 6+ months of follow-up. NRT was provided to participants in the treatment groups in various forms as indicated by the \code{treatment} variable (e.g., gum, patch, inhalator). Note that the dataset includes 136 rows, since a few studies included multiple treatments. } \source{ Hartmann‐Boyce, J., Chepkin, S. C., Ye, W., Bullen, C. & Lancaster, T. (2018). Nicotine replacement therapy versus control for smoking cessation. \emph{Cochrane Database of Systematic Reviews}, \bold{5}, CD000146. \verb{https://doi.org//10.1002/14651858.CD000146.pub5} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hartmannboyce2018 head(dat, 10) \dontrun{ ### load metafor package library(metafor) ### turn treatment into a factor with the desired ordering dat$treatment <- factor(dat$treatment, levels=unique(dat$treatment)) ### meta-analysis per treatment using the M-H method lapply(split(dat, dat$treatment), function(x) rma.mh(measure="RR", ai=x.nrt, n1i=n.nrt, ci=x.ctrl, n2i=n.ctrl, data=x, digits=2)) ### all combined rma.mh(measure="RR", ai=x.nrt, n1i=n.nrt, ci=x.ctrl, n2i=n.ctrl, data=dat, digits=2) } } \keyword{datasets} \concept{medicine} \concept{smoking} \concept{risk ratios} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, smoking, risk ratios, Mantel-Haenszel method } metadat/man/dat.aloe2013.Rd0000644000176200001440000000765714750466700014760 0ustar liggesusers\name{dat.aloe2013} \docType{data} \alias{dat.aloe2013} \title{Studies on the Association Between Supervision Quality and Various Outcomes in Social, Mental Health, and Child Welfare Workers} \description{Results from 5 studies examining the association between various measures of supervision quality and various work-related outcomes in social, mental health, and child welfare workers.} \usage{ dat.aloe2013 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study author(s) and year \cr \bold{n} \tab \code{integer} \tab sample size \cr \bold{tval} \tab \code{numeric} \tab t-statistic for the test of the association/predictor \cr \bold{preds} \tab \code{integer} \tab number of predictors included in the regression model \cr \bold{R2} \tab \code{numeric} \tab the coefficient of determination (i.e., R-squared value) of the regression model \cr } } \details{ The dataset is based on studies that used regression models to examine the association between some measure of perceived supervision quality (e.g., the quality of the relationship with one's supervisor) and some work-related outcome (e.g., job satisfaction) in social, mental health, and child welfare workers. The dataset was extracted from Aloe and Thompson (2013), which in turn is a subset of the studies included in the meta-analysis by Mor Barak et al. (2009). The dataset can be used to illustrate the meta-analysis of regression models, using measures such as the (semi-)partial correlation coefficient. For this, the t-statistic from the regression model for the association (i.e., predictor) of interest was extracted from each regression model (\code{tval}), as well as the sample size (\code{n}), the number of predictors included in the regression model (\code{preds}), and the coefficient of determination (i.e., R-squared value) of the regression model (\code{R2}). Based on this information, the (semi-)partial correlation coefficient can be computed for each study, as well as its corresponding sampling variance. These values can then be meta-analyzed using standard methods. } \source{ Aloe, A. M., & Thompson, C. G. (2013). The synthesis of partial effect sizes. \emph{Journal of the Society for Social Work and Research}, \bold{4}(4), 390--405. \verb{https://doi.org/10.5243/jsswr.2013.24} } \references{ Mor Barak, M. E., Travis, D. J., Pyun, H., & Xie, B. (2009). The impact of supervision on worker outcomes: A meta-analysis. \emph{Social Service Review}, \bold{83}(1), 3--32. \verb{https://doi.org/10.1086/599028} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.aloe2013 dat \dontrun{ ### load metafor package suppressPackageStartupMessages(library(metafor)) ### compute the partial correlation coefficients and corresponding sampling variances dat <- escalc(measure="PCOR", ti=tval, ni=n, mi=preds, data=dat) dat ### random-effects model res <- rma(yi, vi, data=dat) res ### mixed-effects meta-regression model examining the relationship between the partial ### correlation coefficients and the number of predictors included in the models res <- rma(yi, vi, mods = ~ preds, data=dat) res ### compute the r-to-z transformed partial correlation coefficients and their variances dat <- escalc(measure="ZPCOR", ti=tval, ni=n, mi=preds, data=dat) dat ### random-effects model res <- rma(yi, vi, data=dat) res ### back-transformation to the partial correlation scale predict(res, transf=transf.ztor) ### compute the semi-partial correlation coefficients and their variances dat <- escalc(measure="SPCOR", ti=tval, ni=n, mi=preds, r2i=R2, data=dat) dat ### random-effects model res <- rma(yi, vi, data=dat) res } } \keyword{datasets} \concept{social work} \concept{(semi-)partial correlations} \concept{meta-regression} \section{Concepts}{ social work, (semi-)partial correlations, meta-regression } metadat/man/dat.besson2016.Rd0000644000176200001440000002273314750466700015324 0ustar liggesusers\name{dat.besson2016} \docType{data} \alias{dat.besson2016} \title{Dataset on How Maternal Diet Impacts Copying Styles in Rodents} \description{Results from 46 studies synthesising maternal nutritional effects on coping styles in rodents.} \usage{ dat.besson2016 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{comp_ID} \tab \code{character} \tab effect-size unique identifier \cr \bold{study_ID} \tab \code{character} \tab study unique identifier \cr \bold{dam_ID} \tab \code{character} \tab dam unique identifier (group of dams subjected to the same treatment) \cr \bold{animal_ID} \tab \code{character} \tab offspring unique identifier (group of offspring from the same dam group subjected to the same treatment) \cr \bold{Reference} \tab \code{character} \tab author’s names and date \cr \bold{species} \tab \code{character} \tab species [rats or mice] \cr \bold{strain} \tab \code{character} \tab strain \cr \bold{manip_type} \tab \code{character} \tab maternal nutritional manipulation type [protein or calorie] \cr \bold{manip_direction} \tab \code{character} \tab direction of maternal nutritional manipulation [- = restriction, + = overfeeding] \cr \bold{nom_manip_val} \tab \code{character} \tab degree of maternal nutritional manipulation as described in the original publications [\% = percentage of caloric or protein restriction, # = increase in caloric intake] \cr \bold{exp} \tab \code{character} \tab percentage of caloric or protein maternal restriction or increase in caloric intake of the experimental group \cr \bold{control} \tab \code{character} \tab percentage of caloric or protein maternal restriction or increase in caloric intake for the control group \cr \bold{manip_parameter} \tab \code{character} \tab protein content, percentage fat or intake \cr \bold{vitmin_eql} \tab \code{character} \tab were vitamins equalized across maternal diets? [yes or no] \cr \bold{adlib_con} \tab \code{character} \tab were maternal control groups fed ad libitum? [yes or no] \cr \bold{adlib_exp} \tab \code{character} \tab were maternal experimental groups fed ad libitum? [yes or no] \cr \bold{diet_con} \tab \code{character} \tab name of maternal control diet? \cr \bold{diet_exp} \tab \code{character} \tab name of maternal experimental diet? \cr \bold{dam_diet_start_dPC} \tab \code{numeric} \tab start of the dam diet [in days post-conception] \cr \bold{dam_diet_end_dPC} \tab \code{numeric} \tab end of the dam diet [in days post-conception] \cr \bold{diet_label} \tab \code{character} \tab period of maternal diet manipulation [pregestation = pre-gestation, pre = pregnancy, lact = lactation, or pre+lact = pregnancy and lactation] \cr \bold{age_mating} \tab \code{numeric} \tab dam age at mating if known \cr \bold{n_con_dam} \tab \code{integer} \tab sample size of the control dam groups \cr \bold{n_exp_dam} \tab \code{integer} \tab sample size of the experimental dam groups \cr \bold{multi_use_con} \tab \code{character} \tab were control groups used multiple time? [yes or no] \cr \bold{dam_housing} \tab \code{character} \tab how were dams housed? [pair, group, or single] \cr \bold{temperature} \tab \code{numeric} \tab temperature during the experiment [°C] \cr \bold{photoperiod} \tab \code{integer} \tab photoperiod during the experiment [number of hours of light] \cr \bold{litter_size} \tab \code{integer} \tab size of the litter [number of pups per dam] \cr \bold{litter_size_equalized} \tab \code{character} \tab has litter size been equalized? [yes or no] \cr \bold{crossfostered} \tab \code{character} \tab have pups been cross-fostered? [yes or no] \cr \bold{sex} \tab \code{character} \tab sex of the offspring that were tested [m = male, f = female, both = mixed sex] \cr \bold{housing} \tab \code{character} \tab offspring housing during the test period [dam, pair, single, or group] \cr \bold{bodymass_mean_contr} \tab \code{numeric} \tab mean body mass of control offspring close to or during the testing period [g] \cr \bold{bodymass_SE_contr} \tab \code{numeric} \tab S.E. for body mass of control offspring close to or during the testing period \cr \bold{bodymass_mean_exp} \tab \code{numeric} \tab mean body mass of experimental offspring close to or during the testing period [g] \cr \bold{bodymass_SE_exp} \tab \code{numeric} \tab S.E. for body mass of experimental offspring close to or during the testing period \cr \bold{bm_N_contr} \tab \code{integer} \tab sample size for body mass of control offspring close to or during the testing period \cr \bold{bm_N_exp} \tab \code{integer} \tab sample size for body mass of experimental offspring close to or during the testing period \cr \bold{bm_dPP} \tab \code{integer} \tab age of offspring when body mass was measured [in days post-parturition] \cr \bold{offspring_diet} \tab \code{character} \tab offspring diet after weaning [type of control diet] \cr \bold{offspring_con_adlib} \tab \code{character} \tab were control offspring fed ad libitum after weaning? [yes or no] \cr \bold{offspring_diet_level} \tab \code{character} \tab name of offspring diet after weaning \cr \bold{offspring_diet_end_dPP} \tab \code{integer} \tab end of the offspring diet [in days post-parturition] \cr \bold{post_diet_adlib} \tab \code{character} \tab were experimental offspring fed ad libitum after weaning? [yes or no] \cr \bold{response_age_dPP} \tab \code{numeric} \tab offspring age when behavioural testing started [in days post-parturition] \cr \bold{authors_behaviour_classification} \tab \code{character} \tab author's classification of offspring behaviour [anxiety, exploration, or activity] \cr \bold{our_behaviour_classification} \tab \code{character} \tab our classification of offspring behaviour [anxiety, exploration, or activity] \cr \bold{response_test} \tab \code{character} \tab type of test used [elevated T-maze (ETM), open field, etc.] to measure offspring behaviour \cr \bold{time_trial} \tab \code{integer} \tab duration of the testing [min] \cr \bold{measure} \tab \code{character} \tab measures taken during testing [total distance moved, time spent in open arm, etc.] \cr \bold{unit} \tab \code{character} \tab unit of the behavioural measure taken [min, s, m, number (#), etc.] \cr \bold{high_better} \tab \code{character} \tab for activity and exploration, a higher number is assumed to be better (i.e., animals were more active), but the opposite was assumed for anxiety (i.e., they were more anxious) [yes or no] \cr \bold{night.day} \tab \code{character} \tab time of day when behaviours were measured [night or day] \cr \bold{comparison} \tab \code{character} \tab for a given control-treatment group comparison, animal group codes as used in the original article [e.g., LP, HP]. This field allows identification of exactly which data (i.e., comparison of which pairs of groups) were extracted from the original paper, and is not used in our analyses. For our analyses the groups were re-coded as control/experimental. \cr \bold{exp_mean} \tab \code{numeric} \tab mean of the offspring behaviour measured for the experimental group \cr \bold{exp_se} \tab \code{numeric} \tab S.E. of the offspring behaviour measured for the experimental group \cr \bold{exp_n} \tab \code{integer} \tab sample size for the offspring experimental group \cr \bold{con_mean} \tab \code{numeric} \tab mean of offspring behaviour measured for the control group \cr \bold{con_se} \tab \code{numeric} \tab S.E. of the offspring behaviour measured for the control group \cr \bold{con_n} \tab \code{integer} \tab sample size for the offspring control group \cr \bold{con_ID} \tab \code{character} \tab identifier for shared control groups within experiment \cr \bold{percentage} \tab \code{character} \tab is the offspring behaviour measure a percentage? [yes or no] \cr \bold{Data_source} \tab \code{character} \tab figure or table number in the original paper from which the data were extracted \cr \bold{measure_comments} \tab \code{character} \tab any comments on the offspring behaviour measures \cr \bold{SE_imputed} \tab \code{character} \tab was S.E. imputed for the offspring behaviour measure? [yes or no] \cr \bold{Comments} \tab \code{character} \tab any comments on the data \cr } } \details{ Data from experiments where dams were subject to caloric or protein restriction or were overfed around gestation were included. Offspring activity, exploration, or anxiety were measured outcomes variables from maternal experimental treatments. Multilevel meta-analysis and meta-regression models were used to analyze the meta-analytic data. } \source{ Besson, A. A., Lagisz, M., Senior, A. M., Hector, K. L., & Nakagawa, S. (2016). Effect of maternal diet on offspring coping styles in rodents: A systematic review and meta-analysis. \emph{Biological Reviews}, \bold{91}(4), 1065--1080. \verb{https://doi.org/10.1111/brv.12210} } \author{ Daniel Noble, \email{daniel.noble@anu.edu.au} } \examples{ ### copy data into 'dat' and examine data dat <- dat.besson2016 head(dat) \dontrun{ ### load metafor library(metafor) ### compute SD from SE dat$sd_c <- with(dat, con_se * sqrt(con_n)) dat$sd_e <- with(dat, exp_se * sqrt(exp_n)) ### compute standardized mean differences and corresponding sampling variances dat <- escalc(measure="SMD", m1i=exp_mean, m2i=con_mean, sd1i=sd_e, sd2i=sd_c, n1i=exp_n, n2i=con_n, data=dat, add.measure=TRUE) ### fit model mod1 <- rma.mv(yi ~ 1, V = vi, random = list(~ 1 | study_ID, ~ 1 | comp_ID), data = dat) mod1 } } \keyword{datasets} \concept{ecology} \concept{evolution} \concept{standardized mean differences} \section{Concepts}{ ecology, evolution, standardized mean differences } metadat/man/dat.graves2010.Rd0000644000176200001440000000424214750466700015307 0ustar liggesusers\name{dat.graves2010} \docType{data} \alias{dat.graves2010} \title{Studies on the Effectiveness of Injected Cholera Vaccines} \description{Results from 17 studies on the effectiveness of injected vaccines against cholera.} \usage{ dat.graves2010 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab author/study name and publication year \cr \bold{ai} \tab \code{numeric} \tab number of cholera cases in the vaccinated group \cr \bold{n1i} \tab \code{numeric} \tab number of individuals in the vaccinated group \cr \bold{ci} \tab \code{numeric} \tab number of cholera cases in the placebo group \cr \bold{n2i} \tab \code{numeric} \tab number of individuals in the placebo group } } \details{ Cholera is an infection caused by certain strains of the bacterium \emph{Vibrio cholerae}. When untreated, mortality rates can be as high as 50-60\%. Proper sanitation practices are usually effective in preventing outbreaks, but a number of oral and injectable vaccines have also been developed. The Cochrane review by Graves et al. (2010) examined the effectiveness of injectable vaccines for preventing cholera cases and death. The present dataset includes results from 17 studies that reported the number of cholera cases in vaccinated and placebo/comparison groups up to 7 months after the treatment. } \source{ Graves, P. M., Deeks, J. J., Demicheli, V., & Jefferson, T. (2010). Vaccines for preventing cholera: Killed whole cell or other subunit vaccines (injected). \emph{Cochrane Database of Systematic Reviews}, \bold{8}, CD000974. \verb{https://doi.org/10.1002/14651858.CD000974.pub2} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.graves2010 dat \dontrun{ ### load metafor package library(metafor) ### analysis using the Mantel-Haenszel method rma.mh(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, digits=2) } } \keyword{datasets} \concept{medicine} \concept{risk ratios} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, risk ratios, Mantel-Haenszel method } metadat/man/dat.craft2003.Rd0000644000176200001440000000622314750466700015122 0ustar liggesusers\name{dat.craft2003} \docType{data} \alias{dat.craft2003} \title{Studies on the Relationship between the Competitive State Anxiety Inventory-2 and Sport Performance} \description{Results from 10 studies on the relationship between the Competitive State Anxiety Inventory-2 (CSAI-2) and sport performance.} \usage{ dat.craft2003 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{ni} \tab \code{numeric} \tab sample size \cr \bold{sport} \tab \code{character} \tab type of sport (T = team sport, I = individual sport) \cr \bold{ri} \tab \code{numeric} \tab correlation coefficient \cr \bold{var1} \tab \code{character} \tab variable 1 of the correlation coefficient (see \sQuote{Details}) \cr \bold{var2} \tab \code{character} \tab variable 2 of the correlation coefficient (see \sQuote{Details}) } } \details{ The 10 studies included in this dataset are a subset of the studies included in the meta-analysis by Craft et al. (2003) on the relationship between the Competitive State Anxiety Inventory-2 (CSAI-2) and sport performance. The CSAI-2 has three subscales: cognitive anxiety (\code{acog}), somatic anxiety (\code{asom}), and self-confidence (\code{conf}). The studies included in this dataset administered the CSAI-2 prior to some sport competition and then measured sport performance based on the competition. Most studies provided all 6 correlations (3 for the correlations among the 3 subscales and 3 for the correlations between the subscales and sport performance), but 2 studies (with study numbers 6 and 17) only provided a subset. } \source{ Becker, B. J., & Aloe, A. M. (2019). Model-based meta-analysis and related approaches. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), \emph{The handbook of research synthesis and meta-analysis} (3nd ed., pp. 339--363). New York: Russell Sage Foundation. } \references{ Craft, L. L., Magyar, T. M., Becker, B. J., & Feltz, D. L. (2003). The relationship between the Competitive State Anxiety Inventory-2 and sport performance: A meta-analysis. \emph{Journal of Sport and Exercise Psychology}, \bold{25}(1), 44--65. \verb{https://doi.org/10.1123/jsep.25.1.44} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.craft2003 head(dat, 18) \dontrun{ ### load metafor package library(metafor) ### construct dataset and var-cov matrix of the correlations tmp <- rcalc(ri ~ var1 + var2 | study, ni=ni, data=dat) V <- tmp$V dat <- tmp$dat ### examine data for study 1 dat[dat$study == 1,] V[dat$study == 1, dat$study == 1] ### examine data for study 6 dat[dat$study == 6,] V[dat$study == 6, dat$study == 6] ### examine data for study 17 dat[dat$study == 17,] V[dat$study == 17, dat$study == 17] ### multivariate random-effects model res <- rma.mv(yi, V, mods = ~ 0 + var1.var2, random = ~ var1.var2 | study, struct="UN", data=dat) res } } \keyword{datasets} \concept{psychology} \concept{correlation coefficients} \concept{multivariate models} \section{Concepts}{ psychology, correlation coefficients, multivariate models } metadat/man/dat.kearon1998.Rd0000644000176200001440000001261014750466700015325 0ustar liggesusers\name{dat.kearon1998} \docType{data} \alias{dat.kearon1998} \title{Studies on the Accuracy of Venous Ultrasonography for the Diagnosis of Deep Venous Thrombosis} \description{Results from diagnostic accuracy studies examining the accuracy of venous ultrasonography for the diagnosis of deep venous thrombosis.} \usage{ dat.kearon1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study id \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{patients} \tab \code{character} \tab patient group (either symptomatic or asymptomatic patients) \cr \bold{tp} \tab \code{numeric} \tab number of true positives \cr \bold{np} \tab \code{numeric} \tab number of positive patients (cases) \cr \bold{tn} \tab \code{numeric} \tab number of true negatives \cr \bold{nn} \tab \code{numeric} \tab number of negative patients (non-cases) } } \details{ The studies included in the dataset examined the accuracy of venous ultrasonography for the diagnossis of a first deep venous thrombosis in symptomatic and asymptomatic patients. Cases and non-cases were determined based on contrast venography. Venous ultrasonography was then used to make a diagnosis, leading to a given number of true positives and negatives. A subset of this dataset (using only the studies with asymptomatic patients) was used by Deeks et al. (2005) to illustrate methods for detecting publication bias (or small-study effects) in meta-analyses of diagnostic accuracy studies. } \source{ Kearon, C., Julian, J. A., Math, M., Newman, T. E., & Ginsberg, J. S. (1998). Noninvasive diagnosis of deep venous thrombosis. \emph{Annals of Internal Medicine}, \bold{128}(8), 663--677. \verb{https://doi.org/10.7326/0003-4819-128-8-199804150-00011} } \references{ Deeks, J. J., Macaskill, P., & Irwig, L. (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. \emph{Journal of Clinical Epidemiology}, \bold{58}(9), 882--893. \verb{https://doi.org/10.1016/j.jclinepi.2005.01.016} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.kearon1998 head(dat) \dontrun{ ### load metafor package library(metafor) ### calculate diagnostic log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=tp, n1i=np, ci=nn-tn, n2i=nn, data=dat, add=1/2, to="all") head(dat) ### fit random-effects model for the symptomatic patients res <- rma(yi, vi, data=dat, subset=patients=="symptomatic") res ### fit random-effects model for the asymptomatic patients res <- rma(yi, vi, data=dat, subset=patients=="asymptomatic") res ### estimated average diagnostic odds ratio (with 95\% CI) predict(res, transf=exp, digits=2) ### regression test for funnel plot asymmetry using SE as predictor reg <- regtest(res, model="lm") reg ### corresponding funnel plot funnel(res, atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,1.5), steps=4) ys <- seq(0, 2, length=100) lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3) ### regression test for funnel plot asymmetry using total sample size as predictor reg <- regtest(res, model="lm", predictor="ni") reg ### corresponding funnel plot funnel(res, yaxis="ni", atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,300), steps=4) ys <- seq(0, 300, length=100) lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3) ### regression test for funnel plot asymmetry using 1/sqrt(ESS) as predictor (Deeks et al., 2005) dat$invessi <- 1/(4*dat$np) + 1/(4*dat$nn) tmp <- rma(yi, invessi, data=dat, subset=patients=="asymptomatic") reg <- regtest(tmp, model="lm") reg ### corresponding funnel plot funnel(tmp, atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,0.15), steps=4, refline=coef(res), level=0, ylab="1/root(ess)") ys <- seq(0, 0.2, length=100) lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3) ### convert data to long format dat <- to.long(measure="OR", ai=tp, n1i=np, ci=tn, n2i=nn, data=dat.kearon1998, subset=patients=="asymptomatic", append=FALSE) dat$group <- factor(dat$group, levels=c(1,2), labels=c("sensitivity", "specificity")) dat ### calculate logit-transformed sensitivities dat <- escalc(measure="PLO", xi=out1, mi=out2, data=dat, add=1/2, to="all", include=group=="sensitivity") dat ### calculate logit-transformed specificities dat <- escalc(measure="PLO", xi=out1, mi=out2, data=dat, add=1/2, to="all", include=group=="specificity") dat ### bivariate random-effects model for logit sensitivity and specificity res <- rma.mv(yi, vi, mods = ~ 0 + group, random = ~ group | study, struct="UN", data=dat) res ### estimated average sensitivity and specificity based on the model predict(res, newmods = rbind(c(1,0),c(0,1)), transf=transf.ilogit, tau2.levels=c(1,2), digits=2) ### estimated average diagnostic odds ratio based on the model predict(res, newmods = c(1,1), transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{diagnostic accuracy studies} \concept{multivariate models} \concept{publication bias} \section{Concepts}{ medicine, odds ratios, diagnostic accuracy studies, multivariate models, publication bias } metadat/man/dat.obrien2003.Rd0000644000176200001440000001242414750466700015301 0ustar liggesusers\name{dat.obrien2003} \docType{data} \alias{dat.obrien2003} \title{Studies on the Relationship Between BMI and Risk of Preeclampsia} \description{Results from 13 studies on the relationship between maternal body mass index (BMI) and the risk of preeclampsia.} \usage{ dat.obrien2003 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study id \cr \bold{author} \tab \code{character} \tab (first) author of the study \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ref} \tab \code{numeric} \tab reference number \cr \bold{ch} \tab \code{character} \tab exclusion due to chronic hypertension (yes/no) \cr \bold{dm} \tab \code{character} \tab exclusion due to diabetes mellitus (yes/no) \cr \bold{mg} \tab \code{character} \tab exclusion due to multiple gestation (yes/no) \cr \bold{bmi.lb} \tab \code{numeric} \tab lower bound of the BMI interval \cr \bold{bmi.ub} \tab \code{numeric} \tab upper bound of the BMI interval \cr \bold{bmi} \tab \code{numeric} \tab midpoint of the BMI interval \cr \bold{cases} \tab \code{numeric} \tab number of preeclampsia cases in the BMI group \cr \bold{total} \tab \code{numeric} \tab number of individuals in the BMI group } } \details{ The dataset includes the results from 13 studies examining the relationship between maternal body mass index (BMI) and the risk of preeclampsia. For each study, results are given in terms of the number of preeclampsia cases within two or more groups defined by the lower and upper BMI bounds as shown in the dataset (\code{NA} means that the interval is either open to the left or right). The \code{bmi} variable is the interval midpoint as defined by O'Brien et al. (2003). } \source{ O'Brien, T. E., Ray, J. G., & Chan, W.-S. (2003). Maternal body mass index and the risk of preeclampsia: A systematic overview. \emph{Epidemiology}, \bold{14}(3), 368--374. \verb{https://doi.org/10.1097/00001648-200305000-00020} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.obrien2003 dat \dontrun{ ### load metafor package library(metafor) ### restructure the data into a wide format dat2 <- to.wide(dat, study="study", grp="grp", ref=1, grpvars=c("bmi","cases","total"), addid=FALSE, adddesign=FALSE, postfix=c(1,2)) dat2[1:10, -c(2:3)] ### calculate log risk ratios and corresponding sampling variances dat2 <- escalc(measure="RR", ai=cases1, n1i=total1, ci=cases2, n2i=total2, data=dat2) dat2[1:10, -c(2:7)] ### forest plot of the risk ratios dd <- c(0,diff(dat2$study)) dd[dd > 0] <- 1 rows <- (1:nrow(dat2)) + cumsum(dd) rows <- 1 + max(rows) - rows slabs <- mapply(function(x,y,z) as.expression(bquote(.(x)^.(y)~.(z))), dat2$author, dat2$ref, dat2$year) with(dat2, forest(yi, vi, slab=slabs, xlim=c(-7,5.5), cex=0.8, psize=1, pch=19, efac=0, rows=rows, ylim=c(0,max(rows)+3), yaxs="i", atransf=exp, at=log(c(0.05,0.1,0.2,0.5,1,2,5,10,20)), ilab=comp, ilab.xpos=-4, ilab.pos=4)) text(-4.4, max(rows)+2, "Comparison", font=2, cex=0.8, pos=4) ### within-study mean center the BMI variable dat$bmicent <- with(dat, bmi - ave(bmi, study)) ### compute the proportion of preeclampsia cases and corresponding sampling variances dat <- escalc(measure="PR", xi=cases, ni=total, data=dat) ### convert the proportions to percentages (and convert the variances accordingly) dat$yi <- dat$yi*100 dat$vi <- dat$vi*100^2 dat[1:10, -c(2:3)] ### fit multilevel meta-regression model to examine the relationship between the ### (centered) BMI variable and the risk of preeclampsia res <- rma.mv(yi, vi, mods = ~ bmicent, random = ~ 1 | study/grp, data=dat) res ### draw scatterplot with regression line res$slab <- dat$ref regplot(res, xlab=expression("Within-Study Mean Centered BMI"~(kg/m^2)), ylab="Preeclampsia Prevalence (\%)", las=1, bty="l", at=seq(0,18,by=2), olim=c(0,100), psize=2, bg="gray90", label=TRUE, offset=0, labsize=0.6) ### fit model using a random slope for bmicent res <- rma.mv(yi, vi, mods = ~ bmicent, random = ~ bmicent | study, struct="GEN", data=dat) res ### load rms package library(rms) ### fit restricted cubic spline model res <- rma.mv(yi, vi, mods = ~ rcs(bmicent, 4), random = ~ 1 | study/grp, data=dat) res ### get knot positions knots <- attr(rcs(model.matrix(res)[,2], 4), "parms") ### computed predicted values based on the model xs <- seq(-10, 10, length=1000) sav <- predict(res, newmods=rcspline.eval(xs, knots, inclx=TRUE)) ### draw scatterplot with regression line based on the model tmp <- regplot(res, mod=2, pred=sav, xvals=xs, xlab=expression("Within-Study Mean Centered BMI"~(kg/m^2)), ylab="Preeclampsia Prevalence (\%)", las=1, bty="l", at=seq(0,18,by=2), olim=c(0,100), psize=2, bg="gray90", label=TRUE, offset=0, labsize=0.6) abline(v=knots, lty="dotted") points(tmp) } } \keyword{datasets} \concept{medicine} \concept{obstetrics} \concept{risk ratios} \concept{proportions} \concept{multilevel models} \concept{dose-response models} \section{Concepts}{ medicine, obstetrics, risk ratios, proportions, multilevel models, dose-response models } metadat/man/dat.michael2013.Rd0000644000176200001440000001146714750466700015434 0ustar liggesusers\name{dat.michael2013} \docType{data} \alias{dat.michael2013} \title{The Non-Persuasive Power of a Brain Image} \description{Results from studies exploring how a superfluous fMRI brain image influences the persuasiveness of a scientific claim.} \usage{ dat.michael2013 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{Study} \tab \code{character} \tab name of the study: Citation - Experiment - Subgroup \cr \bold{No_brain_n} \tab \code{numeric} \tab sample size for no-brain-image condition \cr \bold{No_brain_m} \tab \code{numeric} \tab mean agreement rating for no-brain-image condition \cr \bold{No_brain_s} \tab \code{numeric} \tab standard deviation for no-brain-image condition \cr \bold{Brain_n} \tab \code{numeric} \tab sample size for brain-image condition \cr \bold{Brain_m} \tab \code{numeric} \tab mean agreement rating for brain-image condition \cr \bold{Brain_s} \tab \code{numeric} \tab standard deviation for brain-image condition \cr \bold{Included_Critique} \tab \code{character} \tab \sQuote{Critique} if article included critical commentary on conclusions, otherwise \sQuote{No_critique} \cr \bold{Medium} \tab \code{character} \tab \sQuote{Paper} if conducted in person; \sQuote{Online} if conducted online \cr \bold{Compensation} \tab \code{character} \tab notes on compensation provided to participants \cr \bold{Participant_Pool} \tab \code{character} \tab notes on where participants were recruited \cr \bold{yi} \tab \code{numeric} \tab raw mean difference, calculated as \code{Brain_m - No_brain_m} \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance \cr } } \details{ The dataset contains the data from the meta-analysis by Michael et al. (2013) of experiments on the persuasive power of a brain image. The meta-analysis analyzed an original study by McCabe and Castel (2008) as well as 10 replication attempts conducted by the authors of the meta-analysis. In each study, participants read an article about using brain imaging as a lie detector. The article either included a superfluous fMRI image of a brain (brain) or not (no_brain). After reading the article, all participants responded to the statement \dQuote{Do you agree or disagree with the conclusion that brain imaging can be used as a lie detector?} on a scale from 1 (strongly disagree) to 4 (strongly agree). The original study by McCabe and Castel (2008) reported a relatively large increase in agreement due to the presence of brain images. Meta-analysis of the original study with the 10 replications suggests, however, a small, possibly null effect: an estimated average raw mean difference of 0.07 points, 95\% CI [-0.00, 0.14], under a random-effects model. In some studies, the article included a passage critiquing the primary claims made in the article; this is coded in the \code{Included_Critique} column for analysis as a possible moderator. Note that Experiment 3 by McCabe and Castel (2008) was a 2x2 between subjects design: brain image presence was manipulated as well as the inclusion of a critique. The two different critique conditions are recorded as separate rows in this dataset. Analysis of this dataset with metafor yields the same results (given rounding) reported in the manuscript. } \source{ Michael, R. B., Newman, E. J., Vuorre, M., Cumming, G., & Garry, M. (2013). On the (non)persuasive power of a brain image. \emph{Psychonomic Bulletin & Review}, \bold{20}(4), 720–-725. \verb{https://doi.org/10.3758/s13423-013-0391-6} } \references{ McCabe, D. P., & Castel, A. D. (2008). Seeing is believing: The effect of brain images on judgments of scientific reasoning. \emph{Cognition}, \bold{107}(1), 343--352. \verb{https://doi.org/10.1016/j.cognition.2007.07.017} } \author{ Robert Calin-Jageman, \email{rcalinjageman@dom.edu}, \url{https://calin-jageman.net} } \examples{ ### copy data into 'dat' and examine data dat <- dat.michael2013 dat \dontrun{ ### load metafor package library(metafor) ### Data prep # yi and vi are already provided, but here's how you would use escalc() to obtain # a raw-mean difference and its variance. # Note the measure parameter is "MD" for 'raw mean difference' dat <- metafor::escalc( measure = "MD", m1i = Brain_m, m2i = No_brain_m, sd1i = Brain_s, sd2i = No_brain_s, n1i = Brain_n, n2i = No_brain_n, data = dat ) ### meta-analysis using a random-effects model of the raw mean differences res <- rma(yi, vi, data=dat) print(res, digits=2) ### examine if Included_Critique is a potential moderator res <- rma(yi, vi, mods = ~ Included_Critique, data=dat) print(res, digits=2) } } \keyword{datasets} \concept{psychology} \concept{persuasion} \concept{raw mean differences} \section{Concepts}{ psychology, persuasion, raw mean differences } metadat/man/dat.baker2009.Rd0000644000176200001440000000643414750466700015121 0ustar liggesusers\name{dat.baker2009} \docType{data} \alias{dat.baker2009} \title{Studies on Pharmacologic Treatments for Chronic Obstructive Pulmonary Disease} \description{Results from 39 trials examining pharmacologic treatments for chronic obstructive pulmonary disease (COPD).} \usage{ dat.baker2009 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{year} \tab \code{numeric} \tab year of publication \cr \bold{id} \tab \code{numeric} \tab study ID \cr \bold{treatment} \tab \code{character} \tab treatment \cr \bold{exac} \tab \code{numeric} \tab number of individuals with one or more COPD exacerbations \cr \bold{total} \tab \code{numeric} \tab number of individuals } } \details{ This dataset comes from a systematic review of randomized controlled trials on pharmacologic treatments for chronic obstructive pulmonary disease (COPD) (Baker et al., 2009). The primary outcome, occurrence of one or more episodes of COPD exacerbation, is binary (yes / no). For this outcome, five drug treatments (fluticasone, budesonide, salmeterol, formoterol, tiotropium) and two combinations (fluticasone + salmeterol, budesonide + formoterol) were compared to placebo. The authors considered the two combinations as separate treatments instead of evaluating the individual components. } \source{ Baker, W. L., Baker, E. L., & Coleman, C. I. (2009). Pharmacologic treatments for chronic obstructive pulmonary disease: A mixed-treatment comparison meta-analysis. \emph{Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy}, \bold{29}(8), 891--905. \verb{https://doi.org/10.1592/phco.29.8.891} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first 6 rows of the dataset head(dat.baker2009) \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Transform data from long arm-based format to contrast-based ### format. Argument 'sm' has to be used for odds ratio as summary ### measure; by default the risk ratio is used in the metabin function ### called internally. pw <- pairwise(treatment, exac, total, studlab = paste(study, year), data = dat.baker2009, sm = "OR") ### Conduct random effects network meta-analysis (NMA) ### with placebo as reference net <- netmeta(pw, fixed = FALSE, ref = "plac") ### Show network graph netgraph(net, seq = "optimal", start = "prcomp", labels = gsub("+", " +\n", trts, fixed = TRUE), plastic = TRUE, thickness = "se.fixed", number = TRUE, points = TRUE, cex.points = 5, col.points = "red", offset = 0.025) ### Print and plot results for network meta-analysis net forest(net) ### Conduct component network meta-analysis (CNMA) cnet <- netcomb(net) cnet ### Compare results of NMA and additive CNMA nb <- netbind(net, cnet, name = c("Standard NMA", "Additive CNMA")) forest(nb) ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{network meta-analysis} \concept{component network meta-analysis} \section{Concepts}{ medicine, odds ratios, network meta-analysis, component network meta-analysis } metadat/man/prep_dat.Rd0000644000176200001440000000165614750466700014552 0ustar liggesusers\name{prep_dat} \alias{prep_dat} \title{Data preparation function} \description{Function to run data processing scripts.} \usage{ prep_dat(rebuild=FALSE, overwrite, pkgdir) } \arguments{ \item{rebuild}{logical indicating whether the entire database should be rebuild (default is \code{FALSE}).} \item{overwrite}{character vector with one or more \code{.Rd} filenames to overwrite (if they already exist). The default is to never overwrite any existing \code{.Rd} files.} \item{pkgdir}{character string specifying the root directory of the source package (if unspecified, the current working directory is assumed to be the package root directory).} } \details{ The function is only for used for processing new datasets for inclusion in the package. It should be used as described on the \pkg{\link{metadat-package}} help page. } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org} } \keyword{file} \keyword{internal} metadat/man/dat.anand1999.Rd0000644000176200001440000000661714750466700015142 0ustar liggesusers\name{dat.anand1999} \docType{data} \alias{dat.anand1999} \title{Studies on the Effectiveness of Oral Anticoagulants in Patients with Coronary Artery Disease} \description{Results from 34 trials examining the effectiveness of oral anticoagulants in patients with coronary artery disease.} \usage{ dat.anand1999 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab author(s) or trial name \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{intensity} \tab \code{character} \tab intensity of anticoagulation (low, medium, or high) \cr \bold{asp.t} \tab \code{numeric} \tab concomitant use of aspirin in the treatment group (0 = no, 1 = yes) \cr \bold{asp.c} \tab \code{numeric} \tab concomitant use of aspirin in the control group (0 = no, 1 = yes) \cr \bold{ai} \tab \code{numeric} \tab number of deaths in the treatment group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the treatment group \cr \bold{ci} \tab \code{numeric} \tab number of deaths in the control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the control group } } \details{ The dataset includes the results from 34 randomized clinical trials that examined the effectiveness of oral anticoagulants in patients with coronary artery disease. The results given here are focused on the total mortality in the treatment versus control groups. } \note{ Strictly speaking, there are only 31 trials, since Breddin et al. (1980) and ATACS (1990) are multiarm trials. According to a correction, \code{dat.anand1999$ci[29]} should be 1. But then \code{dat.anand1999$ci[21]} would also have to be 1 (if these data indeed refer to the same control group). This appears contradictory, so this correction was not made. } \source{ Anand, S. S., & Yusuf, S. (1999). Oral anticoagulant therapy in patients with coronary artery disease: A meta-analysis. \emph{Journal of the American Medical Association}, \bold{282}(21), 2058--2067. \verb{https://doi.org/10.1001/jama.282.21.2058} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.anand1999 dat \dontrun{ ### load metafor package library(metafor) ### High-Intensity OA vs Control rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(intensity=="high" & asp.t==0 & asp.c==0), digits=2) ### High- or Moderate-Intensity OA vs Aspirin rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(intensity \%in\% c("high","moderate") & asp.t==0 & asp.c==1), digits=2) ### Moderate-Intensity OA vs Control rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(intensity=="moderate" & asp.t==0 & asp.c==0), digits=2) ### High- or Moderate-Intensity OA and Aspirin vs Aspirin rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(intensity \%in\% c("high","moderate") & asp.t==1 & asp.c==1), digits=2) ### Low-Intensity OA and Aspirin vs Aspirin rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(intensity=="low" & asp.t==1 & asp.c==1), digits=2) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{odds ratios} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, cardiology, odds ratios, Mantel-Haenszel method } metadat/man/dat.nakagawa2007.Rd0000644000176200001440000000320314750466700015574 0ustar liggesusers\name{dat.nakagawa2007} \docType{data} \alias{dat.nakagawa2007} \title{Assessing the Function of House Sparrows' Bib Size Using a Flexible Meta-Analysis Method} \description{A meta-analysis on the association between the size of a male's bib and their social status in house sparrows (\emph{Passer domesticus}).} \usage{ dat.nakagawa2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{StudyID} \tab \code{character} \tab identity of primary study \cr \bold{Place} \tab \code{character} \tab location of study population \cr \bold{Correlation} \tab \code{numeric} \tab correlation coefficient \cr \bold{SampleSize} \tab \code{integer} \tab sample size of population \cr } } \details{ Each study measures the association between a sparrows bib size and its social status. Effects are quantified as correlation coefficients. } \source{ Nakagawa, S., Ockendon, N., Gillespie, D. O. S, Hatchwell, B. J., & Burke, T. (2007). Assessing the function of house sparrows' bib size using a flexible meta-analysis method. \emph{Behavioral Ecology}, \bold{18}(5), 831--840. \verb{https://doi.org/10.1093/beheco/arm050} } \author{ Daniel Noble, \email{daniel.noble@anu.edu.au} } \examples{ ### copy data into 'dat' and examine data dat <- dat.nakagawa2007 dat \dontrun{ ### load metafor package library(metafor) ### calculate Zr dat <- escalc(measure="ZCOR", ri=Correlation, ni=SampleSize, data=dat) ### fit meta-analytic model res <- rma.mv(yi, vi, random = ~ 1 | StudyID, data=dat) res } } \keyword{datasets} \concept{ecology} \concept{correlation coefficients} \section{Concepts}{ ecology, correlation coefficients } metadat/man/dat.spooner2002.Rd0000644000176200001440000000627114750466700015512 0ustar liggesusers\name{dat.spooner2002} \docType{data} \alias{dat.spooner2002} \title{Studies on Nedocromil Sodium for Preventing Exercise-Induced Bronchoconstriction} \description{Results from 17 trials, 11 studies in children and 6 studies in adults, reporting the maximum fall in the forced expiratory volume in 1 second (FEV_1) over the course of follow-up, expressed as a percentage.} \usage{ dat.spooner2002 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab first author \cr \bold{year} \tab \code{character} \tab year of publication \cr \bold{Ne} \tab \code{integer} \tab number of participants in nedocromil sodium group \cr \bold{Me} \tab \code{numeric} \tab maximum fall in the FEV_1 (nedocromil sodium) \cr \bold{Se} \tab \code{numeric} \tab standard deviation (nedocromil sodium) \cr \bold{Nc} \tab \code{integer} \tab number of participants in placebo group \cr \bold{Mc} \tab \code{numeric} \tab maximum fall in the FEV_1 (placebo) \cr \bold{Sc} \tab \code{numeric} \tab standard deviation (placebo) \cr \bold{agegroup} \tab \code{factor} \tab age group (children or adults) \cr } } \details{ Spooner et al. (2002) conducted a Cochrane review comparing nedocromil sodium (experimental treatment) with placebo (control) for preventing exercise-induced bronchoconstriction. Primary outcome was the maximum fall in the forced expiratory volume in 1 second (FEV_1) over the course of follow-up, expressed as a percentage. This outcome is available for 17 studies, 11 studies in children and 6 studies in adults. For each study, the mean value, standard deviation, and sample size are reported for both the experimental and control group. The authors conducted a random-effects meta-analysis with the mean difference as effect measure, i.e.\ mean value in the nedocromil sodium group minus mean value in the placebo group. This data set is used as an example in Schwarzer et al. (2015). } \source{ Spooner, C., Saunders, L. D., & Rowe, B. H. (2002). Nedocromil sodium for preventing exercise‐induced bronchoconstriction. \emph{Cochrane Database of Systematic Reviews}, \bold{1}, CD001183. \verb{https://doi.org/10.1002/14651858.CD001183} } \references{ Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). \emph{Meta-analysis with R}. Cham, Switzerland: Springer. } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first five studies head(dat.spooner2002, 5) \dontrun{ ### Load meta package suppressPackageStartupMessages(library("meta")) ### Use settings from RevMan5 oldset <- settings.meta("RevMan5") ### Conduct random effects meta-analysis with age subgroups mc1 <- metacont(Ne, Me, Se, Nc, Mc, Sc, data = dat.spooner2002, studlab = paste(author, year), subgroup = agegroup, print.subgroup.name = FALSE, label.e = "Nedocromil sodium", label.c = "Placebo", common = FALSE) mc1 ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{raw mean differences} \concept{subgroup analysis} \section{Concepts}{ raw mean differences, subgroup analysis } metadat/man/dat.gibson2002.Rd0000644000176200001440000001375714750466700015315 0ustar liggesusers\name{dat.gibson2002} \docType{data} \alias{dat.gibson2002} \title{Studies on the Effectiveness of Self-Management Education and Regular Medical Review for Adults with Asthma} \description{Results from 15 trials examining the effectiveness of self-management education and regular medical review for adults with asthma.} \usage{ dat.gibson2002 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab first author of study \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{n1i} \tab \code{numeric} \tab number of participants in the intervention group \cr \bold{m1i} \tab \code{numeric} \tab mean number of days off work/school in the intervention group \cr \bold{sd1i} \tab \code{numeric} \tab standard deviation of the number of days off work/school in the intervention group \cr \bold{n2i} \tab \code{numeric} \tab number of participants in the control/comparison group \cr \bold{m2i} \tab \code{numeric} \tab mean number of days off work/school in the control/comparison group \cr \bold{sd2i} \tab \code{numeric} \tab standard deviation of the number of days off work/school in the control/comparison group \cr \bold{ai} \tab \code{numeric} \tab number of participants who had one or more days off work/school in the intervention group \cr \bold{bi} \tab \code{numeric} \tab number of participants who no days off work/school in the intervention group \cr \bold{ci} \tab \code{numeric} \tab number of participants who had one or more days off work/school in the control/comparison group \cr \bold{di} \tab \code{numeric} \tab number of participants who no days off work/school in the control/comparison group \cr \bold{type} \tab \code{numeric} \tab numeric code for the intervention type (see \sQuote{Details}) } } \details{ Asthma management guidelines typically recommend for patients to receive education and regular medical review. While self-management programs have been shown to increase patient knowledge, it is less clear to what extent they actually impact health outcomes. The systematic review by Gibson et al. (2002) examined the effectiveness of self-management education and regular medical review for adults with asthma. In each study, participants receiving a certain management intervention were compared against those in a control/comparison group with respect to a variety of health outcomes. One of the outcomes examined in a number of studies was the number of days off work/school. The majority of studies reporting this outcome provided means and standard deviations allowing a meta-analysis of standardized mean differences. Seven studies also reported the number of participants who had one or more days off work/school in each group. These studies could be meta-analyzed using, for example, (log) risk ratios. Finally, one could also consider a combined analysis based on standardized mean differences computed from the means and standard deviations where available and using probit transformed risk differences (which also provide estimates of the standardized mean difference) for the remaining studies. Some degree of patient education was provided in all studies. In addition, the \code{type} variable indicates what additional intervention components were included in each study: \enumerate{ \item optimal self-management (writing action plan, self-monitoring, regular medical review), \item self-monitoring and regular medical review, \item self-monitoring only, \item regular medical review only, \item written action plan only. } } \source{ Gibson, P. G., Powell, H., Wilson, A., Abramson, M. J., Haywood, P., Bauman, A., Hensley, M. J., Walters, E. H., & Roberts, J. J. L. (2002). Self-management education and regular practitioner review for adults with asthma. \emph{Cochrane Database of Systematic Reviews}, \bold{3}, CD001117. \verb{https://doi.org/10.1002/14651858.CD001117} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.gibson2002 dat \dontrun{ ### load metafor package library(metafor) ### compute standardized mean differences and corresponding sampling variances dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) dat ### fit an equal-effects model to the standardized mean differences (as in Gibson et al., 2002) res <- rma(yi, vi, data=dat, method="EE") print(res, digits=2) ### compute log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat) dat ### fit an equal-effects model to the log risk ratios res <- rma(yi, vi, data=dat, method="EE") print(res, digits=2) predict(res, transf=exp, digits=2) ### note: Gibson et al. (2002) used the Mantel-Haenszel method for their analysis rma.mh(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat, digits=2) ### compute standardized mean differences where possible and otherwise probit transformed ### risk differences (which also provide estimates of the standardized mean differences) dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat, add.measure=TRUE) dat <- escalc(measure="PBIT", ai=ai, bi=bi, ci=ci, di=di, data=dat, replace=FALSE, add.measure=TRUE) dat ### fit a random-effects model to these estimates res <- rma(yi, vi, data=dat) print(res, digits=2) ### meta-regression model examining if there are systematic differences based on the ### type of measure used (there are only 2 studies where measure="PBIT", so this isn't ### very conclusive here, but shown for illustration purposes) res <- rma(yi, vi, mods = ~ measure, data=dat) print(res, digits=2) predict(res, newmods=1, digits=2) } } \keyword{datasets} \concept{medicine} \concept{primary care} \concept{risk ratios} \concept{standardized mean differences} \section{Concepts}{ medicine, primary care, risk ratios, standardized mean differences } metadat/man/dat.frank2008.Rd0000644000176200001440000001123714750466700015132 0ustar liggesusers\name{dat.frank2008} \docType{data} \alias{dat.frank2008} \title{Studies on the Association Between the CASP8 -652 6N del Promoter Polymorphism and Breast Cancer Risk} \description{Results from 4 case-control studies examining the association between the CASP8 -652 6N del promoter polymorphism and breast cancer risk.} \usage{ dat.frank2008 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study identifier \cr \bold{bc.ins.ins} \tab \code{numeric} \tab number of cases who have a homozygous insertion polymorphism \cr \bold{bc.ins.del} \tab \code{numeric} \tab number of cases who have a heterozygous insertion/deletion polymorphism \cr \bold{bc.del.del} \tab \code{numeric} \tab number of cases who have a homozygous deletion polymorphism \cr \bold{ct.ins.ins} \tab \code{numeric} \tab number of controls who have a homozygous insertion polymorphism \cr \bold{ct.ins.del} \tab \code{numeric} \tab number of controls who are heterozygous insertion/deletion polymorphism \cr \bold{ct.del.del} \tab \code{numeric} \tab number of controls who have a homozygous deletion polymorphism } } \details{ The 4 studies included in this dataset are case-control studies that have examined the association between the CASP8 -652 6N del promoter polymorphism and breast cancer risk. Breast cancer cases and controls were genotyped and either had a homozygous insertion, a heterozygous insertion/deletion, or a homozygous deletion polymorphism. Ziegler et al. (2011) used the same dataset to illustrate the use of meta-analytic methods to examine deviations from Hardy-Weinberg equilibrium across multiple studies. The relative excess heterozygosity (REH) is the proposed measure for such a meta-analysis, which can be computed by setting \code{measure="REH"}. } \source{ Frank, B., Rigas, S. H., Bermejo, J. L., Wiestler, M., Wagner, K., Hemminki, K., Reed, M. W., Sutter, C., Wappenschmidt, B., Balasubramanian, S. P., Meindl, A., Kiechle, M., Bugert, P., Schmutzler, R. K., Bartram, C. R., Justenhoven, C., Ko, Y.-D., Brüning, T., Brauch, H., Hamann, U., Pharoah, P. P. D., Dunning, A. M., Pooley, K. A., Easton, D. F., Cox, A. & Burwinkel, B. (2008). The CASP8 -652 6N del promoter polymorphism and breast cancer risk: A multicenter study. \emph{Breast Cancer Research and Treatment}, \bold{111}(1), 139--144. \verb{https://doi.org/10.1007/s10549-007-9752-z} } \references{ Ziegler, A., Steen, K. V. & Wellek, S. (2011). Investigating Hardy-Weinberg equilibrium in case-control or cohort studies or meta-analysis. \emph{Breast Cancer Research and Treatment}, \bold{128}(1), 197--201. \verb{https://doi.org/10.1007/s10549-010-1295-z} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.frank2008 dat \dontrun{ ### load metafor package library(metafor) ### calculate log odds ratios comparing ins/del versus ins/ins dat <- escalc(measure="OR", ai=bc.ins.del, bi=bc.ins.ins, ci=ct.ins.del, di=ct.ins.ins, data=dat) ### fit random-effects model and get the pooled odds ratio (with 95\% CI) res <- rma(yi, vi, data=dat) res predict(res, transf=exp, digits=2) ### calculate log odds ratios comparing del/del versus ins/ins dat <- escalc(measure="OR", ai=bc.del.del, bi=bc.ins.ins, ci=ct.del.del, di=ct.ins.ins, data=dat) ### fit random-effects model and get the pooled odds ratio (with 95\% CI) res <- rma(yi, vi, data=dat) res predict(res, transf=exp, digits=2) ### calculate log odds ratios comparing ins/del+del/del versus ins/ins dat <- escalc(measure="OR", ai=bc.ins.del+bc.del.del, bi=bc.ins.ins, ci=ct.ins.del+ct.del.del, di=ct.ins.ins, data=dat) ### fit random-effects model and get the pooled odds ratio (with 95\% CI) res <- rma(yi, vi, data=dat) res predict(res, transf=exp, digits=2) ############################################################################ ### compute the relative excess heterozygosity in the controls dat <- escalc(measure="REH", ai=ct.ins.ins, bi=ct.ins.del, ci=ct.del.del, slab=study, data=dat) ### fit random-effects model and get the pooled REH value (with 90\% CI) res <- rma(yi, vi, data=dat, level=90) res predict(res, transf=exp, digits=2) ### draw forest plot forest(res, atransf=exp, xlim=c(-1.4,1.4), at=log(c(0.5,5/7,1,7/5,2))) segments(log(5/7), -2, log(5/7), res$k+1, lty="dotted") segments(log(7/5), -2, log(7/5), res$k+1, lty="dotted") } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{genetics} \concept{odds ratios} \section{Concepts}{ medicine, oncology, genetics, odds ratios } metadat/man/dat.maire2019.Rd0000644000176200001440000001043414750466700015126 0ustar liggesusers\name{dat.maire2019} \docType{data} \alias{dat.maire2019} \title{Studies on Temporal Trends in Fish Community Structures in French Rivers} \description{Results from studies examining changes in the abundance of fish species in French rivers.} \usage{ dat.maire2019 } \format{ The object is a list containing a data frame called \code{dat} that contains the following columns and distance matrix called \code{dmat}: \tabular{lll}{ \bold{site} \tab \code{character} \tab study site \cr \bold{station} \tab \code{character} \tab sampling station at site \cr \bold{site_station} \tab \code{character} \tab site and station combined \cr \bold{s1} \tab \code{numeric} \tab Mann-Kendal trend statistic for relative abundance of non-local species \cr \bold{vars1} \tab \code{numeric} \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr \bold{s2} \tab \code{numeric} \tab Mann-Kendal trend statistic for relative abundance of northern species \cr \bold{vars2} \tab \code{numeric} \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr \bold{s3} \tab \code{numeric} \tab Mann-Kendal trend statistic for relative abundance of non-native species \cr \bold{vars3} \tab \code{numeric} \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr \bold{const} \tab \code{numeric} \tab constant value of 1 } } \details{ The dataset includes the results from 35 sampling stations (at 11 sites along various French rivers) examining the abundance of various fish species over time (i.e., over 19-37 years, all until 2015). The temporal trend in these abundance data was quantified in terms of Mann-Kendal trend statistics, with positive values indicating monotonically increasing trends. The corresponding sampling variances were corrected for the temporal autocorrelation in the data (Hamed & Rao, 1998). The distance matrix \code{dmat} indicates the distance of the sampling stations (1-423 river-km). For stations not connected through the river network, a high distance value of 10,000 river-km was set (effectively forcing the spatial correlation to be 0 for such stations). The dataset can be used to illustrate a meta-analysis allowing for spatial correlation in the outcomes. } \source{ Maire, A., Thierry, E., Viechtbauer, W., & Daufresne, M. (2019). Poleward shift in large-river fish communities detected with a novel meta-analysis framework. \emph{Freshwater Biology}, \bold{64}(6), 1143--1156. \verb{https://doi.org/10.1111/fwb.13291} } \references{ Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. \emph{Journal of Hydrology}, \bold{204}(1-4), 182--196. \verb{https://doi.org/10.1016/S0022-1694(97)00125-X} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.maire2019$dat dat[-10] ### copy distance matrix into 'dmat' and examine first 5 rows/columns dmat <- dat.maire2019$dmat dmat[1:5,1:5] \dontrun{ ### load metafor package library(metafor) ### fit a standard random-effects model ignoring spatial correlation res1 <- rma.mv(s1, vars1, random = ~ 1 | site_station, data=dat) res1 ### fit model allowing for spatial correlation res2 <- rma.mv(s1, vars1, random = ~ site_station | const, struct="SPGAU", data=dat, dist=list(dmat), control=list(rho.init=10)) res2 ### add random effects for sites and stations within sites res3 <- rma.mv(s1, vars1, random = list(~ 1 | site/station, ~ site_station | const), struct="SPGAU", data=dat, dist=list(dmat), control=list(rho.init=10)) res3 ### likelihood ratio tests comparing the models anova(res1, res2) anova(res2, res3) ### profile likelihood plots for model res2 profile(res2, cline=TRUE) ### effective range (river-km for which the spatial correlation is >= 0.05) sqrt(3) * res2$rho ### note: it was necessary to adjust the starting value for rho in models ### res2 and res3 so that the optimizer does not get stuck in a local maximum profile(res2, rho=1, xlim=c(0,200), steps=100) } } \keyword{datasets} \concept{ecology} \concept{climate change} \concept{spatial correlation} \section{Concepts}{ ecology, climate change, spatial correlation } metadat/man/dat.damico2009.Rd0000644000176200001440000000445414750466700015271 0ustar liggesusers\name{dat.damico2009} \docType{data} \alias{dat.damico2009} \title{Studies on Topical plus Systemic Antibiotics to Prevent Respiratory Tract Infections} \description{Results from 16 studies examining the effectiveness of topical plus systemic antibiotics to prevent respiratory tract infections (RTIs).} \usage{ dat.damico2009 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab first author \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{xt} \tab \code{numeric} \tab number of RTIs in the treatment group \cr \bold{nt} \tab \code{numeric} \tab number of patients in the treatment group \cr \bold{xc} \tab \code{numeric} \tab number of RTIs in the control group \cr \bold{nc} \tab \code{numeric} \tab number of patients in the control group \cr \bold{conceal} \tab \code{numeric} \tab allocation concealment (0 = not adequate, 1 = adequate) \cr \bold{blind} \tab \code{numeric} \tab blinding (0 = open, 1 = double-blind) } } \details{ The dataset includes the results from 16 studies that examined the effectiveness of topical plus systemic antibiotics versus no prophylaxis to prevent respiratory tract infections (RTIs). } \source{ D'Amico, R., Pifferi, S., Torri, V., Brazzi, L., Parmelli, E., & Liberati, A. (2009). Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. \emph{Cochrane Database of Systematic Reviews}, \bold{4}, CD000022. \verb{https://doi.org/10.1002/14651858.CD000022.pub3} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.damico2009 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of the (log) odds ratios using the Mantel-Haenszel method rma.mh(measure="OR", ai=xt, n1i=nt, ci=xc, n2i=nc, data=dat, digits=2) ### calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=xt, n1i=nt, ci=xc, n2i=nc, data=dat) ### meta-analysis using a random-effects model res <- rma(yi, vi, data=dat, method="DL") res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \section{Concepts}{ medicine, odds ratios } metadat/man/dat.normand1999.Rd0000644000176200001440000000577214750466700015520 0ustar liggesusers\name{dat.normand1999} \docType{data} \alias{dat.normand1999} \title{Studies on the Length of Hospital Stay of Stroke Patients} \description{Results from 9 studies on the length of the hospital stay of stroke patients under specialized care and under conventional/routine (non-specialist) care.} \usage{ dat.normand1999 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{source} \tab \code{character} \tab source of data \cr \bold{n1i} \tab \code{numeric} \tab number of patients under specialized care \cr \bold{m1i} \tab \code{numeric} \tab mean length of stay (in days) under specialized care \cr \bold{sd1i} \tab \code{numeric} \tab standard deviation of the length of stay under specialized care \cr \bold{n2i} \tab \code{numeric} \tab number of patients under routine care \cr \bold{m2i} \tab \code{numeric} \tab mean length of stay (in days) under routine care \cr \bold{sd2i} \tab \code{numeric} \tab standard deviation of the length of stay under routine care } } \details{ The 9 studies provide data in terms of the mean length of the hospital stay (in days) of stroke patients under specialized care and under conventional/routine (non-specialist) care. The goal of the meta-analysis was to examine the hypothesis whether specialist stroke unit care will result in a shorter length of hospitalization compared to routine management. } \source{ Normand, S. T. (1999). Meta-analysis: Formulating, evaluating, combining, and reporting. \emph{Statistics in Medicine}, \bold{18}(3), 321--359. \verb{https://doi.org/10.1002/(sici)1097-0258(19990215)18:3<321::aid-sim28>3.0.co;2-p} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.normand1999 dat \dontrun{ ### load metafor package library(metafor) ### calculate mean differences and corresponding sampling variances dat <- escalc(measure="MD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) dat ### meta-analysis of mean differences using a random-effects model res <- rma(yi, vi, data=dat) res ### meta-analysis of standardized mean differences using a random-effects model res <- rma(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat, slab=source) res ### draw forest plot forest(res, xlim=c(-7,5), alim=c(-3,1), header="Study/Source") ### calculate (log transformed) ratios of means and corresponding sampling variances dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) dat ### meta-analysis of the (log transformed) ratios of means using a random-effects model res <- rma(yi, vi, data=dat) res predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{raw mean differences} \concept{standardized mean differences} \section{Concepts}{ medicine, raw mean differences, standardized mean differences } metadat/man/dat.vanhowe1999.Rd0000644000176200001440000001032114750466700015513 0ustar liggesusers\name{dat.vanhowe1999} \docType{data} \alias{dat.vanhowe1999} \title{Studies on the Association between Circumcision and HIV Infection} \description{Results from 33 studies examining the association between male circumcision and HIV infection. \loadmathjax} \usage{ dat.vanhowe1999 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study author \cr \bold{category} \tab \code{character} \tab study type (high-risk group, partner study, or population survey) \cr \bold{non.pos} \tab \code{numeric} \tab number of non-circumcised HIV positive cases \cr \bold{non.neg} \tab \code{numeric} \tab number of non-circumcised HIV negative cases \cr \bold{cir.pos} \tab \code{numeric} \tab number of circumcised HIV positive cases \cr \bold{cir.neg} \tab \code{numeric} \tab number of circumcised HIV negative cases } } \details{ The 33 studies provide data in terms of \mjeqn{2 \times 2}{2x2} tables in the form: \tabular{lcc}{ \tab HIV positive \tab HIV negative \cr non-circumcised \tab \code{non.pos} \tab \code{non.neg} \cr circumcised \tab \code{cir.pos} \tab \code{cir.neg} } The goal of the meta-analysis was to examine if the risk of an HIV infection differs between non-circumcised versus circumcised men. The dataset is interesting because it can be used to illustrate the difference between naively pooling results by summing up the counts across studies and then computing the odds ratio based on the aggregated table (as was done by Van Howe, 1999) and conducting a proper meta-analysis (as illustrated by O'Farrell & Egger, 2000). In fact, a proper meta-analysis shows that the HIV infection risk is on average higher in non-circumcised men, which is the opposite of what the naive pooling approach yields (which makes this an illustration of Simpson's paradox). } \source{ Van Howe, R. S. (1999). Circumcision and HIV infection: Review of the literature and meta-analysis. \emph{International Journal of STD & AIDS}, \bold{10}(1), 8--16. \verb{https://doi.org/10.1258/0956462991913015} } \references{ O'Farrell, N., & Egger, M. (2000). Circumcision in men and the prevention of HIV infection: A 'meta-analysis' revisited. \emph{International Journal of STD & AIDS}, \bold{11}(3), 137--142. \verb{https://doi.org/10.1258/0956462001915480} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.vanhowe1999 dat \dontrun{ ### load metafor package library(metafor) ### naive pooling by summing up the counts within categories and then ### computing the odds ratios and corresponding confidence intervals cat1 <- with(dat[dat$category=="high-risk group",], escalc(measure="OR", ai=sum(non.pos), bi=sum(non.neg), ci=sum(cir.pos), di=sum(cir.neg))) cat2 <- with(dat[dat$category=="partner study",], escalc(measure="OR", ai=sum(non.pos), bi=sum(non.neg), ci=sum(cir.pos), di=sum(cir.neg))) cat3 <- with(dat[dat$category=="population survey",], escalc(measure="OR", ai=sum(non.pos), bi=sum(non.neg), ci=sum(cir.pos), di=sum(cir.neg))) summary(cat1, transf=exp, digits=2) summary(cat2, transf=exp, digits=2) summary(cat3, transf=exp, digits=2) ### naive pooling across all studies all <- escalc(measure="OR", ai=sum(dat$non.pos), bi=sum(dat$non.neg), ci=sum(dat$cir.pos), di=sum(dat$cir.neg)) summary(all, transf=exp, digits=2) ### calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=non.pos, bi=non.neg, ci=cir.pos, di=cir.neg, data=dat) dat ### random-effects model res <- rma(yi, vi, data=dat, method="DL") res predict(res, transf=exp, digits=2) ### random-effects model within subgroups res <- rma(yi, vi, data=dat, method="DL", subset=category=="high-risk group") predict(res, transf=exp, digits=2) res <- rma(yi, vi, data=dat, method="DL", subset=category=="partner study") predict(res, transf=exp, digits=2) res <- rma(yi, vi, data=dat, method="DL", subset=category=="population survey") predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{epidemiology} \concept{odds ratios} \section{Concepts}{ medicine, epidemiology, odds ratios } metadat/man/dat.bakdash2021.Rd0000644000176200001440000003005214750466700015415 0ustar liggesusers\name{dat.bakdash2021} \docType{data} \alias{dat.bakdash2021} \title{Dataset on Situation Awareness and Task Performance Associations} \description{Results from 77 papers with 678 effects evaluating associations among measures of situation awareness and task performance.} \usage{ dat.bakdash2021 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{Author} \tab \code{character} \tab paper author(s) \cr \bold{Year} \tab \code{integer} \tab year of paper publication \cr \bold{Title} \tab \code{character} \tab title of paper \cr \bold{DOI} \tab \code{character} \tab digital object identifier (DOI) \cr \bold{DTIC.link} \tab \code{character} \tab permanent link for Defense Technical Information Collection (DITC) reports; see: \verb{https://www.dtic.mil} \cr \bold{SA.measure.type} \tab \code{character} \tab type of SA measure \cr \bold{Sample.size} \tab \code{integer} \tab reported sample size \cr \bold{Sample.size.stats} \tab \code{integer} \tab reported sample size based on reported statistics (this reflects excluded participants) \cr \bold{es.z} \tab \code{numeric} \tab z-transformed correlation coefficient; includes ghost results (disclosed and undisclosed non-significant effects not reported in detail) imputed using the draw method described in Bakdash et al. (2021a) \cr \bold{vi.z} \tab \code{numeric} \tab variance for z-transformed correlation (calculated using \code{Sample.size.stats}, \emph{not} \code{Sample.size}) \cr \bold{SampleID} \tab \code{character} \tab unique identifier for each experiment/study \cr \bold{Outcome} \tab \code{integer} \tab unique value for each effect size } } \details{ The dataset contains behavioral experiments from 77 papers/79 studies with a total of 678 effects, evaluating associations among measures of situation awareness (\dQuote{knowing what is going on}) and task performance. Examples of situation awareness include knowledge of current vehicle speed in a simulated driving task and location and heading of aircraft in a simulated air traffic control task. Corresponding examples of task performance include \dQuote{the number of collisions in a simulated driving task} and \dQuote{subject matter expert rating of conflict management in a simulated air control task} (Bakdash et al. 2021a, p. 2). This dataset and the \sQuote{Examples} are a highly simplified version of the data and code in Bakdash et al. (2021b; 2021c). The journal article by Bakdash et al. (2021a) describes the systematic review and meta-analysis in detail. This dataset is used to illustrate multilevel multivariate meta-analytic models for the overall pooled effect and pooled effects by situation awareness measure. We also adjust meta-analytic models using cluster-robust variance estimation / cluster-robust inference with the \code{\link[metafor]{robust}} function in \emph{metafor}. Results are shown graphically in a customized forest plot with a prediction interval (estimated plausible range of individual effects). Last, we create a table summarizing the estimated meta-analytic heterogeneity parameters. The meta-analytic results show most pooled effect sizes in the positive medium range or less. There was also substantial meta-analytic heterogeneity (estimated systematic variance in true effects), nearing the magnitude of the overall pooled effect. We interpret the meta-analytic results as situation awareness typically having limited validity for task performance (i.e., good situation awareness does not tend to have strong probabilistic links with good performance and vice-versa). More formally, measures of situation awareness do not generally and meaningfully capture cognitive processes and other relevant factors underlying task performance. \subsection{Run-Time}{ The code run-time can be greatly sped-up using a linear algebra library with \emph{R} that makes use of multiple CPU cores. See: \url{https://www.metafor-project.org/doku.php/tips:speeding_up_model_fitting}. To measure the run-time, uncomment these three lines: \code{start.time <- Sys.time()}, \code{end.time <- Sys.time()}, and \code{end.time - start.time}. Run-times on Windows 10 x64 with the Intel Math Kernel Library are: \tabular{rll}{ \tab \emph{CPU} \tab \emph{Run-Time (Minutes)} \cr \tab i7-11850H \tab 2.49 \cr \tab i7-4770 \tab 5.38 \cr } } } \source{ Bakdash, J. Z., Marusich, L. R., Cox, K. R., Geuss, M. N., Zaroukian, E. G., & Morris, K. M. (2021b). The validity of situation awareness for performance: A meta-analysis (Code Ocean Capsule). \verb{https://doi.org/10.24433/CO.1682542.v4} Bakdash, J. Z., Marusich, L. R., Cox, K. R., Geuss, M. N., Zaroukian, E. G., & Morris, K. M. (2021c). The validity of situation awareness for performance: A meta-analysis (Systematic Review, Data, and Code). \verb{https://doi.org/10.17605/OSF.IO/4K7ZV} } \references{ Bakdash, J. Z., Marusich, L. R., Cox, K. R., Geuss, M. N., Zaroukian, E. G., & Morris, K. M. (2021a). The validity of situation awareness for performance: A meta-analysis. \emph{Theoretical Issues in Ergonomics Science}, 1--24. \verb{https://doi.org/10.1080/1463922X.2021.1921310} Supplemental materials: \verb{https://www.tandfonline.com/doi/suppl/10.1080/1463922X.2021.1921310/suppl_file/ttie_a_1921310_sm5524.docx} } \author{ Jonathan Bakdash, \email{jonathan.z.bakdash.civ@army.mil}, \email{jbakdash@gmail.com} \cr Laura Marusich, \email{laura.m.cooper20.civ@army.mil}, \email{lmarusich@gmail.com} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bakdash2021 head(dat[c(1,2,6,8:12)]) \dontrun{ #start.time <- Sys.time() ### load metafor library(metafor) ### multilevel meta-analytic model to get the overall pooled effect res.overall <- rma.mv(es.z, vi.z, mods = ~ 1, random = ~ 1 | SampleID / Outcome, data = dat, test = "t") res.overall ### get prediction interval predict(res.overall) ### cluster-robust variance estimation (CRVE) / cluster-robust inference res.overall.crve <- robust(res.overall, cluster = SampleID) res.overall.crve ### get prediction interval res.overall.crve.pred <- predict(res.overall.crve) res.overall.crve.pred ### multilevel meta-analytic model for SA measures res.sa <- rma.mv(es.z, vi.z, mods = ~ 0 + SA.measure.type, random = ~ 1 | SampleID / Outcome, data = dat, test = "t") res.sa ### cluster-robust variance estimation (CRVE) / cluster-robust inference res.sa.crve <- robust(res.sa, cluster = SampleID) res.sa.crve ### profile likelihood plots par(mfrow=c(2,1)) profile(res.sa.crve, progbar = FALSE) ### format and combine output of meta-analytic models for the forest plot all.z <- c(res.sa.crve$beta, # SA measures res.overall.crve$beta, # pooled effect for confidence interval (CI) res.overall.crve$beta) # pooled effect for prediction interval (PI) all.ci.lower <- c(res.sa.crve$ci.lb, # SA measures res.overall.crve.pred$ci.lb, # pooled effect, lower CI res.overall.crve.pred$pi.lb) # pooled effect, lower PI all.ci.upper <- c(res.sa.crve$ci.ub, # SA measures res.overall.crve.pred$ci.ub, # pooled effect, upper CI res.overall.crve.pred$pi.ub) # pooled effect, upper PI ### note: there is no p-value for the PI all.pvals <- c(res.sa.crve$pval, res.overall.crve$pval) all.labels <- c(sort(unique(dat$SA.measure.type)), "Overall", "95\% Prediction Interval") ### function to round p-values for the forest plot pvals.round <- function(input) { input <- ifelse(input < 0.001, "< 0.001", ifelse(input < 0.01, "< 0.01", ifelse(input < 0.05 & input >= 0.045, "< 0.05", ifelse(round(input, 2) == 1.00, "0.99", sprintf("\%.2f", round(input, 2))))))} all.pvals.rounded <- pvals.round(all.pvals) ### forest plot plot.vals <- data.frame(all.labels, all.z, all.ci.lower, all.ci.upper) par(mfrow=c(1,1), cex = 1.05) forest(plot.vals$all.z, ci.lb = plot.vals$all.ci.lower, ci.ub = plot.vals$all.ci.upper, slab = plot.vals$all.labels, psize = 1, efac = 0, xlim = c(-1.8, 2.5), clim = c(-1, 1), transf = transf.ztor, # transform z to r at = seq(-0.5, 1, by = 0.25), xlab = expression("Correlation Coefficient"~"("*italic('r')*")"), main = "\n\n\nSA Measures", ilab = c(all.pvals.rounded, ""), ilab.xpos = 2.45, ilab.pos = 2.5, digits = 2, refline = 0, annotate = FALSE, header = FALSE) ### keep trailing zero using sprintf output <- cbind(sprintf("\%.2f", round(transf.ztor(plot.vals$all.z), 2)), sprintf("\%.2f", round(transf.ztor(plot.vals$all.ci.lower), 2)), sprintf("\%.2f", round(transf.ztor(plot.vals$all.ci.upper), 2))) ### alignment kludge annotext <- apply(output, 1, function(x) {paste0(" ", x[1], " [", x[2],", ", x[3], "]")}) text( 1.05, 12:1, annotext, pos = 4, cex = 1.05) text(-1.475, 14.00, "SA Measure", cex = 1.05) text( 2.30, 14.00, substitute(paste(italic('p-value'))), cex = 1.05) text( 1.55, 14.00, "Correlation [95\% CI]", cex = 1.05) abline(h = 2.5) ### black polygon for overall mean CIs addpoly(all.z[11], ci.lb = all.ci.lower[11], ci.ub = all.ci.upper[11], rows = 2, annotate = FALSE, efac = 1.5, transf = transf.ztor) ### white polygon for PI addpoly(all.z[12], ci.lb = all.ci.lower[12], ci.ub = all.ci.upper[12], rows = 1, col = "white", border = "black", annotate = FALSE, efac = 1.5, transf = transf.ztor) par(mfrow=c(1,1), cex = 1) # reset graph parameters to default ### confidence intervals for the variance components re.CI.variances <- confint(res.overall) re.CI.variances sigma1.z <- data.frame(re.CI.variances[[1]]["random"]) sigma2.z <- data.frame(re.CI.variances[[2]]["random"]) ### fit model using alternative multivariate parameterization res.overall.alt <- rma.mv(es.z, vi.z, mods = ~ 1, random = ~ factor(Outcome) | factor(SampleID), data = dat, test = "t") ### confidence intervals for the total amount of heterogeneity variance component res.overall.alt.tau <- confint(res.overall.alt, tau2=1)$random ### I^2: http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate W <- diag(1/dat$vi.z) X <- model.matrix(res.overall) P <- W - W \%*\% X \%*\% solve(t(X) \%*\% W \%*\% X) \%*\% t(X) \%*\% W ### I^2 (variance due to heterogeneity): 61\% I2 <- 100 * res.overall.alt$tau2 / (res.overall.alt$tau2 + (res.overall$k-res.overall$p)/sum(diag(P))) I2 ### 95\% CI for I^2 using uncertainty around tau^2 I2.CI.lb <- 100 * res.overall.alt.tau[1,2] / (res.overall.alt.tau[1,2] + (res.overall$k-res.overall$p)/sum(diag(P))) I2.CI.lb I2.CI.ub <- 100 * res.overall.alt.tau[1,3] / (res.overall.alt.tau[1,3] + (res.overall$k-res.overall$p)/sum(diag(P))) I2.CI.ub ### total amount of heterogeneity (tau) sqrt(res.overall.alt$tau2) ### heterogeneity table table.heterogeneity <- data.frame(matrix(ncol = 3, nrow = 4)) colnames(table.heterogeneity) <- c("Parameter Value", "Lower 95\% CI", "Upper 95\% CI") rownames(table.heterogeneity) <- c("Tau (Total)", "Tau1 (Between paper)", "Tau2 (Within paper)", "I2 (\%)") table.heterogeneity[1,] <- res.overall.alt.tau[2,] table.heterogeneity[2,] <- sigma1.z[2,] table.heterogeneity[3,] <- sigma2.z[2,] table.heterogeneity[4,] <- c(I2, I2.CI.lb, I2.CI.ub) round(table.heterogeneity, 2) #end.time <- Sys.time() #end.time - start.time } } \keyword{datasets} \concept{psychology} \concept{human factors} \concept{engineering} \concept{correlation coefficients} \concept{multilevel models} \concept{multivariate models} \concept{cluster-robust inference} \section{Concepts}{ psychology, human factors, engineering, correlation coefficients, multilevel models, multivariate models, cluster-robust inference } metadat/man/dat.lehmann2018.Rd0000644000176200001440000002364214750466700015457 0ustar liggesusers\name{dat.lehmann2018} \docType{data} \alias{dat.lehmann2018} \title{The Effect of Red on Perceived Attractiveness} \description{Results from studies in which participants rated the attractiveness of photos that featured red or a control color. See OSF project at \verb{https://osf.io/xy47p/}.} \usage{ dat.lehmann2018 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{Short_Title} \tab \code{character} \tab Shortened citation formatted as: Author name(s), year of publication - Experiment number. All cells in the column are unique for use as labels in the meta-analysis. \cr \bold{Full_Citation} \tab \code{character} \tab Full citation in APA format. \cr \bold{Short_Citation} \tab \code{character} \tab Shortened citation of different format, exactly as it would appear in an in-text citation. \cr \bold{Year} \tab \code{numeric} \tab Year study published (whether in journal or published online). \cr \bold{Study} \tab \code{character} \tab Experiment number. If only one experiment presented in a paper, then \sQuote{Exp 1}, otherwise numbered according to numbering within paper. \cr \bold{Peer_Reviewed} \tab \code{character} \tab Whether the experiment was published in a peer-reviewed journal or not. \sQuote{Yes} = peer-reviewed journal, \sQuote{No} can mean in press, online publication, or other. \cr \bold{Source_Type} \tab \code{character} \tab Location where experiment is available, including journal articles, conference proceedings, online-only, and other options. More specific than whether peer-reviewed or not. \cr \bold{Preregistered} \tab \code{character} \tab Whether experiment was pre-registered or not. \cr \bold{Moderator_Group} \tab \code{character} \tab In some studies, a moderator was intentionally investigated that was meant to reduce the red-romance effect. Data for studies where the red-romance effect is expected to be moderated are marked \sQuote{Yes} in this column. All others are blank. \cr \bold{Gender} \tab \code{character} \tab Gender of rater (male or female). In all cases, gender of stimuli will be opposite. \cr \bold{Color_Contrast} \tab \code{character} \tab The color used as the contrast against red. In some cases, not every contrast color was listed. We chose to examine only contrasts that were present in the original studies, when possible. This column contains only the contrasts we examined in this meta-analysis. \cr \bold{Color_Form} \tab \code{character} \tab Location of color in photo. Background = background or border color manipulated; Face = facial redness manipulated; Shirt, Dress, Item = color of specified object manipulated; Dot = a dot of color on shirt manipulated. \cr \bold{Photo_Type} \tab \code{character} \tab Amount of body visible in photo. Head Shot = head only; Bust = head, shoulders, sometimes torso; Full Body = entire body visible. \cr \bold{DV_Type} \tab \code{character} \tab Scale used for DV. \sQuote{Perceived attractiveness} = the perceived attractiveness scale used in the original studies; alternate scales are differentiated. \cr \bold{DV_Items} \tab \code{numeric} \tab Number of items in DV scale. \cr \bold{DV_Scale} \tab \code{character} \tab Full length of DV scale, if clear. \cr \bold{DV_ScaleBottom} \tab \code{numeric} \tab Lower anchor of DV scale. \cr \bold{DV_ScaleTop} \tab \code{numeric} \tab Upper anchor of DV scale. \cr \bold{Location} \tab \code{character} \tab Country where study took place, if clear. \sQuote{Worldwide} in some cases of online participation without IP filtering of participants. \cr \bold{Continent} \tab \code{character} \tab Continent where study took place, for the sake of creating larger categories for analysis. \cr \bold{Participants} \tab \code{character} \tab Basic notes about participants. Students = high school, undergraduate, or graduate students; Online = participants were gathered online; Adult = no other common identifying factor given. \cr \bold{Participant_Notes} \tab \code{character} \tab A finer grained description of participant characteristics. \cr \bold{Design} \tab \code{character} \tab Whether study was a between- or within-subjects design. \cr \bold{Eth_Majority} \tab \code{character} \tab Basic notes about participant ethnicity for ease of analysis. This represents the ethnic majority within the sample. \cr \bold{Eth_Majority_Detail} \tab \code{character} \tab A finer grained description of participant characteristics, including in some cases participant counts when the ethnic majority was close to another category. \cr \bold{Eth_Stim} \tab \code{character} \tab Ethnicity of the people pictured in the stimulus materials. \cr \bold{Eth_Match} \tab \code{character} \tab Whether the ethnic majority of the participant pool matched the ethnicity of stimulus photos. \cr \bold{Red_Age} \tab \code{numeric} \tab Mean age of participants in red group. If not given for specific group, then mean age overall. \cr \bold{Control_Age} \tab \code{numeric} \tab Mean age of participants in control group. If not given for specific group, then mean age overall. \cr \bold{Color_Red} \tab \code{character} \tab Specific values of red color, if given. \cr \bold{Color_Control} \tab \code{character} \tab Specific values of control color, if given. \cr \bold{Red_Original} \tab \code{character} \tab Whether the red color used in the study is within 5 units of the LCh values for red used in the original study. \cr \bold{Color_Match} \tab \code{character} \tab Whether the control color used in the study is within 5 units of the red color on the L and C parameters. In cases where the control color used was white, it was not possible for the L and C parameters to match. \cr \bold{Presentation_Control} \tab \code{character} \tab Whether the color of the stimulus viewed by each participant was consistent, as in participants viewing everything on paper or the same computer, versus uncontrolled presentation of the stimulus, as in viewing stimulus on different computers. \cr \bold{Stimuli_Presentation} \tab \code{character} \tab Method for presenting stimuli. \sQuote{Paper} = stimuli printed on paper, shown in-person; \sQuote{Screen} = stimuli shown on-screen, not carefully controlled; \sQuote{Screen Control} = stimuli shown on-screen, but screen carefully color-matched. \cr \bold{Red_N} \tab \code{numeric} \tab Number of participants in red group. \cr \bold{Red_M} \tab \code{numeric} \tab Mean rating of DV in red group. \cr \bold{Red_SD} \tab \code{numeric} \tab Standard deviation of DV in red group. \cr \bold{Control_N} \tab \code{numeric} \tab Number of participants in control group. \cr \bold{Control_M} \tab \code{numeric} \tab Mean rating of DV in control group. \cr \bold{Control_SD} \tab \code{numeric} \tab Standard deviation of DV in control group. \cr \bold{SD_diff} \tab \code{numeric} \tab Calculated for within-subjects studies, standard deviation of difference scores. \cr \bold{RM_r} \tab \code{numeric} \tab Calculated for within-subjects studies, correlation between participant ratings of red and control attractiveness. \cr \bold{Control_Attractiveness} \tab \code{numeric} \tab Attractiveness of stimuli in control condition, calculated as \code{(Control_M - DV_ScaleBottom) / DV_ScaleTop}, in order to compare attractiveness ratings across different scales. \cr \bold{Notes} \tab \code{character} \tab Any additional notes on the study. \cr \bold{Total.SampleSize} \tab \code{numeric} \tab Total unique participants in the study. \cr \bold{pooled} \tab \code{numeric} \tab Pooled standard deviation for within-subjects studies. \cr \bold{yi} \tab \code{numeric} \tab Standardized mean difference. \cr \bold{vi} \tab \code{numeric} \tab Corresponding sampling variance. \cr } } \details{ This is data from a meta-analysis of studies that test the red-romance hypothesis, which is that the color red enhances heterosexual attraction in romantic contexts. Analyzing male participants only, the meta-analysis should show a small, statistically significant effect (d = 0.26 [0.12, 0.40], p = .0004, N = 2,961). Analyzing female participants only should show a very small effect (d = 0.13 [0.01, 0.25], p = .03, N = 2,739). The analyses in the published meta-analysis found clear evidence of upward bias in the estimate for female participants and equivocal evidence for male participants. Moderator analyses suggest effect sizes may have declined over time (both genders), may be largest when an original shade of red is used (men only), and may be smaller in pre-registered studies (women only). } \source{ Lehmann, G. K., Elliot, A. J., & Calin-Jageman, R. J. (2018). Meta-analysis of the effect of red on perceived attractiveness. \emph{Evolutionary Psychology}, \bold{16}(4). \verb{https://doi.org/10.1177/1474704918802412} \verb{https://osf.io/xy47p/} } \author{ Robert Calin-Jageman, \email{rcalinjageman@dom.edu}, \url{https://calin-jageman.net} } \examples{ ### copy data into 'dat' and examine data dat <- dat.lehmann2018 head(dat) \dontrun{ ### load metafor package library(metafor) ### meta-analyses for male and female participants red_romance_malep <- dat[dat$Gender == "Males", ] red_romance_femalep <- dat[dat$Gender == "Females", ] res_malep <- rma(yi, vi, data=red_romance_malep, test="knha") res_malep res_femalep <- rma(yi, vi, data=red_romance_femalep, test="knha") res_femalep } } \keyword{datasets} \concept{psychology} \concept{attraction} \concept{standardized mean differences} \section{Concepts}{ psychology, attraction, standardized mean differences } metadat/man/dat.molloy2014.Rd0000644000176200001440000001004614750466700015336 0ustar liggesusers\name{dat.molloy2014} \docType{data} \alias{dat.molloy2014} \title{Studies on the Relationship between Conscientiousness and Medication Adherence} \description{Results from 16 studies on the correlation between conscientiousness and medication adherence.} \usage{ dat.molloy2014 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{authors} \tab \code{character} \tab study authors \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ni} \tab \code{numeric} \tab sample size of the study \cr \bold{ri} \tab \code{numeric} \tab observed correlation \cr \bold{controls} \tab \code{character} \tab number of variables controlled for \cr \bold{design} \tab \code{character} \tab whether a cross-sectional or prospective design was used \cr \bold{a_measure} \tab \code{character} \tab type of adherence measure (self-report or other) \cr \bold{c_measure} \tab \code{character} \tab type of conscientiousness measure (NEO or other) \cr \bold{meanage} \tab \code{numeric} \tab mean age of the sample \cr \bold{quality} \tab \code{numeric} \tab methodological quality } } \details{ Conscientiousness, one of the big-5 personality traits, can be defined as \dQuote{socially prescribed impulse control that facilitates task- and goal-directed behaviour, such as thinking before acting, delaying gratification, following norms and rules and planning, organising and prioritising tasks} (John & Srivastava, 1999). Conscientiousness has been shown to be related to a number of health-related behaviors (e.g., tobacco/alcohol/drug use, diet and activity patterns, risky behaviors). A recent meta-analysis by Molloy et al. (2014) examined to what extent conscientiousness is related to medication adherence, that is, the extent to which (typically chronically ill) patients follow a prescribed medication regimen (e.g., taking a daily dose of a cholesterol lowering drug in patients with high LDL serum cholesterol levels). The results from the 16 studies included in this meta-analysis are provided in this dataset. Variable \code{a_measure} indicates whether adherence was measured based on self-reports or a more \sQuote{objective} measure (e.g., electronic monitoring of pill bottle openings, pill counts). Variable \code{c_measure} indicates whether conscientiousness was measured with some version of the NEO personality inventory or some other scale. Methodological quality was scored by the authors on a 1 to 4 scale with higher scores indicating higher quality (see article for details on how this score was derived). } \source{ Molloy, G. J., O'Carroll, R. E., & Ferguson, E. (2014). Conscientiousness and medication adherence: A meta-analysis. \emph{Annals of Behavioral Medicine}, \bold{47}(1), 92--101. \verb{https://doi.org/10.1007/s12160-013-9524-4} } \references{ John, O. P., & Srivastava, S. (1999). The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), \emph{Handbook of personality: Theory and research} (2nd ed., pp. 102-138). New York: Guilford Press. } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.molloy2014 dat[-c(5:6)] \dontrun{ ### load metafor package library(metafor) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, slab=paste(authors, year, sep=", ")) dat[-c(5:6)] ### meta-analysis of the transformed correlations using a random-effects model res <- rma(yi, vi, data=dat) res ### average correlation with 95\% CI predict(res, digits=3, transf=transf.ztor) ### forest plot forest(res, addpred=TRUE, xlim=c(-1.6,1.6), atransf=transf.ztor, at=transf.rtoz(seq(-0.4, 0.6, by=0.2)), digits=c(2,1), cex=0.9, header="Author(s), Year") ### funnel plot funnel(res) } } \keyword{datasets} \concept{psychology} \concept{medicine} \concept{correlation coefficients} \section{Concepts}{ psychology, medicine, correlation coefficients } metadat/man/dat.landenberger2005.Rd0000644000176200001440000001277314750466700016464 0ustar liggesusers\name{dat.landenberger2005} \docType{data} \alias{dat.landenberger2005} \title{Studies on the Effectiveness of CBT for Reducing Recidivism} \description{Results from 58 studies on the effectiveness of cognitive-behavioral therapy (CBT) for reducing recidivism in juvenile and adult offenders.} \usage{ dat.landenberger2005 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab (first) author and year \cr \bold{pubtype} \tab \code{character} \tab publication type (book chapter, journal article, report, or thesis) \cr \bold{country} \tab \code{character} \tab country where study was carried out (Canada, New Zealand, UK, or USA) \cr \bold{design} \tab \code{character} \tab study design (matched groups, nonequivalent groups, or randomized trial) \cr \bold{program} \tab \code{character} \tab purpose of setting up the CBT program (for demonstration, practice, or research purposes) \cr \bold{setting} \tab \code{character} \tab treatment setting (community or prison) \cr \bold{designprob} \tab \code{character} \tab indication of study design problems (no, favors the control group, or favors the treatment group) \cr \bold{n.ctrl.rec} \tab \code{numeric} \tab number of recidivists in the control group \cr \bold{n.ctrl.non} \tab \code{numeric} \tab number of non-recidivists in the control group \cr \bold{n.cbt.rec} \tab \code{numeric} \tab number of recidivists in the CBT group \cr \bold{n.cbt.non} \tab \code{numeric} \tab number of non-recidivists in the CBT group \cr \bold{interval} \tab \code{numeric} \tab recidivism interval (in months) \cr \bold{group} \tab \code{numeric} \tab study group (adults or juveniles) \cr \bold{age} \tab \code{numeric} \tab mean age of the study group \cr \bold{male} \tab \code{numeric} \tab percentage of males in the study group \cr \bold{minority} \tab \code{numeric} \tab percentage of minorities in the study group \cr \bold{length} \tab \code{numeric} \tab treatment length (in weeks) \cr \bold{sessions} \tab \code{numeric} \tab number of CBT sessions per week \cr \bold{hrs_week} \tab \code{numeric} \tab treatment hours per week \cr \bold{hrs_total} \tab \code{numeric} \tab total hours of treatment \cr \bold{cbt.cogskills} \tab \code{character} \tab CBT component: cognitive skills (yes, no) \cr \bold{cbt.cogrestruct} \tab \code{character} \tab CBT component: cognitive restructuring (yes, no) \cr \bold{cbt.intpprbsolv} \tab \code{character} \tab CBT component: interpersonal problem solving (yes, no) \cr \bold{cbt.socskills} \tab \code{character} \tab CBT component: social skills (yes, no) \cr \bold{cbt.angerctrl} \tab \code{character} \tab CBT component: anger control (yes, no) \cr \bold{cbt.victimimpact} \tab \code{character} \tab CBT component: victim impact (yes, no) \cr \bold{cbt.subabuse} \tab \code{character} \tab CBT component: substance abuse (yes, no) \cr \bold{cbt.behavmod} \tab \code{character} \tab CBT component: behavior modification (yes, no) \cr \bold{cbt.relapseprev} \tab \code{character} \tab CBT component: relapse prevention (yes, no) \cr \bold{cbt.moralrsng} \tab \code{character} \tab CBT component: moral reasoning (yes, no) \cr \bold{cbt.roletaking} \tab \code{character} \tab CBT component: role taking (yes, no) \cr \bold{cbt.other} \tab \code{character} \tab CBT component: other (yes, no) } } \details{ Landenberger and Lipsey (2005) conducted a meta-analysis of 58 experimental and quasi-experimental studies of the effects of cognitive-behavioral therapy (CBT) on the recidivism rates of adult and juvenile offenders (see also Lipsey et al., 2007). The present dataset includes the results of these studies and a range of potential moderator variables to identify factors associated with variation in treatment effects. } \source{ Personal communication. } \references{ Landenberger, N. A., & Lipsey, M. W. (2005). The positive effects of cognitive-behavioral programs for offenders: A meta-analysis of factors associated with effective treatment. \emph{Journal of Experimental Criminology}, \bold{1}, 451--476. \verb{https://doi.org/10.1007/s11292-005-3541-7} Lipsey, M. W., Landenberger, N. A., & Wilson, S. J. (2007). Effects of cognitive-behavioral programs for criminal offenders. \emph{Campbell Systematic Reviews}, \bold{3}(1), 1--27. \verb{https://doi.org/10.4073/csr.2007.6} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.landenberger2005 head(dat) \dontrun{ ### load metafor package library(metafor) ### calculate log odds ratios (for non-recidivism in CBT vs. control groups) and sampling variances dat <- escalc(measure="OR", ai=n.cbt.non, bi=n.cbt.rec, ci=n.ctrl.non, di=n.ctrl.rec, data=dat) ### fit random-effects model res <- rma(yi, vi, data=dat) res ### estimated average OR and corresponding 95\% CI/PI predict(res, transf=exp, digits=2) ### examine if number of treatment sessions per week is a potential moderator res <- rma(yi, vi, mods = ~ sessions, data=dat) res ### predicted ORs for 1, 2, 5, or 10 sessions per week predict(res, newmods=c(1,2,5,10), transf=exp, digits=2) } } \keyword{datasets} \concept{psychology} \concept{criminology} \concept{odds ratios} \concept{meta-regression} \section{Concepts}{ psychology, criminology, odds ratios, meta-regression } metadat/man/dat.lopez2019.Rd0000644000176200001440000001520414750466700015162 0ustar liggesusers\name{dat.lopez2019} \docType{data} \alias{dat.lopez2019} \title{Studies on the Effectiveness of CBT for Depression} \description{Results from 76 studies examining the effectiveness of cognitive behavioral therapy (CBT) for depression in adults.} \usage{ dat.lopez2019 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab (first) author and year of study \cr \bold{treatment} \tab \code{character} \tab treatment provided (see \sQuote{Details}) \cr \bold{scale} \tab \code{character} \tab scale used to measure depression symptoms \cr \bold{n} \tab \code{numeric} \tab group size \cr \bold{diff} \tab \code{numeric} \tab standardized mean change \cr \bold{se} \tab \code{numeric} \tab corresponding standard error \cr \bold{group} \tab \code{numeric} \tab type of therapy (0 = individual, 1 = group therapy) \cr \bold{tailored} \tab \code{numeric} \tab whether the intervention was tailored to each patient (0 = no, 1 = yes) \cr \bold{sessions} \tab \code{numeric} \tab number of sessions \cr \bold{length} \tab \code{numeric} \tab average session length (in minutes) \cr \bold{intensity} \tab \code{numeric} \tab product of sessions and length \cr \bold{multi} \tab \code{numeric} \tab intervention included multimedia elements (0 = no, 1 = yes) \cr \bold{cog} \tab \code{numeric} \tab intervention included cognitive techniques (0 = no, 1 = yes) \cr \bold{ba} \tab \code{numeric} \tab intervention included behavioral activation (0 = no, 1 = yes) \cr \bold{psed} \tab \code{numeric} \tab intervention included psychoeducation (0 = no, 1 = yes) \cr \bold{home} \tab \code{numeric} \tab intervention included homework (0 = no, 1 = yes) \cr \bold{prob} \tab \code{numeric} \tab intervention included problem solving (0 = no, 1 = yes) \cr \bold{soc} \tab \code{numeric} \tab intervention included social skills training (0 = no, 1 = yes) \cr \bold{relax} \tab \code{numeric} \tab intervention included relaxation (0 = no, 1 = yes) \cr \bold{goal} \tab \code{numeric} \tab intervention included goal setting (0 = no, 1 = yes) \cr \bold{final} \tab \code{numeric} \tab intervention included a final session (0 = no, 1 = yes) \cr \bold{mind} \tab \code{numeric} \tab intervention included mindfulness (0 = no, 1 = yes) \cr \bold{act} \tab \code{numeric} \tab intervention included acceptance and commitment therapy (0 = no, 1 = yes) } } \details{ The dataset includes the results from 76 studies examining the effectiveness of cognitive behavioral therapy (CBT) for treating depression in adults. Studies included two or more of the following treatments/conditions: \enumerate{ \item treatment as usual (TAU), \item no treatment, \item wait list, \item psychological or attention placebo, \item face-to-face CBT, \item multimedia CBT, \item hybrid CBT (i.e., multimedia CBT with one or more face-to-face sessions). } Multimedia CBT was defined as CBT delivered via self-help books, audio/video recordings, telephone, computer programs, apps, e-mail, or text messages. Variable \code{diff} is the standardized mean change within each group, with negative values indicating a decrease in depression symptoms. } \source{ Personal communication. } \references{ López-López, J. A., Davies, S. R., Caldwell, D. M., Churchill, R., Peters, T. J., Tallon, D., Dawson, S., Wu, Q., Li, J., Taylor, A., Lewis, G., Kessler, D. S., Wiles, N., & Welton, N. J. (2019). The process and delivery of CBT for depression in adults: A systematic review and network meta-analysis. \emph{Psychological Medicine}, \bold{49}(12), 1937--1947. \verb{https://doi.org/10.1017/S003329171900120X} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.lopez2019 dat[1:10,1:6] \dontrun{ ### load metafor package library(metafor) ### create network graph ('igraph' package must be installed) library(igraph, warn.conflicts=FALSE) pairs <- data.frame(do.call(rbind, sapply(split(dat$treatment, dat$study), function(x) t(combn(x,2)))), stringsAsFactors=FALSE) pairs$X1 <- factor(pairs$X1, levels=sort(unique(dat$treatment))) pairs$X2 <- factor(pairs$X2, levels=sort(unique(dat$treatment))) tab <- table(pairs[,1], pairs[,2]) tab # adjacency matrix g <- graph_from_adjacency_matrix(tab, mode = "plus", weighted=TRUE, diag=FALSE) plot(g, edge.curved=FALSE, edge.width=E(g)$weight/2, layout=layout_in_circle(g, order=c("Wait list", "No treatment", "TAU", "Multimedia CBT", "Hybrid CBT", "F2F CBT", "Placebo")), vertex.size=45, vertex.color="lightgray", vertex.label.color="black", vertex.label.font=2) ### restructure data into wide format dat <- to.wide(dat, study="study", grp="treatment", ref="TAU", grpvars=c("diff","se","n"), postfix=c("1","2")) ### compute contrasts between treatment pairs and corresponding sampling variances dat$yi <- with(dat, diff1 - diff2) dat$vi <- with(dat, se1^2 + se2^2) ### calculate the variance-covariance matrix for multitreatment studies calc.v <- function(x) { v <- matrix(x$se2[1]^2, nrow=nrow(x), ncol=nrow(x)) diag(v) <- x$vi v } V <- bldiag(lapply(split(dat, dat$study), calc.v)) ### add contrast matrix to the dataset dat <- contrmat(dat, grp1="treatment1", grp2="treatment2") ### network meta-analysis using a contrast-based random-effects model ### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons ### the treatment left out (TAU) becomes the reference level for the treatment comparisons res <- rma.mv(yi, V, data=dat, mods = ~ 0 + No.treatment + Wait.list + Placebo + F2F.CBT + Hybrid.CBT + Multimedia.CBT, random = ~ comp | study, rho=1/2) res ### forest plot of the contrast estimates (treatments versus TAU) forest(coef(res), diag(vcov(res)), slab=sub(".", " ", names(coef(res)), fixed=TRUE), xlim=c(-5,5), alim=c(-3,3), psize=1, header="Treatment", xlab="Difference in Standardized Mean Change (compared to TAU)") ### fit random inconsistency effects model (might have to switch optimizer to get convergence) res <- rma.mv(yi, V, data=dat, mods = ~ 0 + No.treatment + Wait.list + Placebo + F2F.CBT + Hybrid.CBT + Multimedia.CBT, random = list(~ comp | study, ~ comp | design), rho=1/2, phi=1/2, control=list(optimizer="BFGS")) res } } \keyword{datasets} \concept{psychiatry} \concept{standardized mean changes} \concept{network meta-analysis} \section{Concepts}{ psychiatry, standardized mean changes, network meta-analysis } metadat/man/dat.nielweise2007.Rd0000644000176200001440000000613614750466700016016 0ustar liggesusers\name{dat.nielweise2007} \docType{data} \alias{dat.nielweise2007} \title{Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections} \description{Results from 18 studies comparing the risk of catheter-related bloodstream infection when using anti-infective-treated versus standard catheters in the acute care setting.} \usage{ dat.nielweise2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{author} \tab \code{character} \tab (first) author \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ai} \tab \code{numeric} \tab number of CRBSIs in patients receiving an anti-infective catheter \cr \bold{n1i} \tab \code{numeric} \tab number of patients receiving an anti-infective catheter \cr \bold{ci} \tab \code{numeric} \tab number of CRBSIs in patients receiving a standard catheter \cr \bold{n2i} \tab \code{numeric} \tab number of patients receiving a standard catheter } } \details{ The use of a central venous catheter may lead to a catheter-related bloodstream infection (CRBSI), which in turn increases the risk of morbidity and mortality. Anti-infective-treated catheters have been developed that are meant to reduce the risk of CRBSIs. Niel-Weise et al. (2007) conducted a meta-analysis of studies comparing infection risk when using anti-infective-treated versus standard catheters in the acute care setting. The results from 18 such studies are included in this dataset. The dataset was used in the article by Stijnen et al. (2010) to illustrate various generalized linear mixed-effects models for the meta-analysis of proportions and odds ratios (see \sQuote{References}). } \source{ Niel-Weise, B. S., Stijnen, T., & van den Broek, P. J. (2007). Anti-infective-treated central venous catheters: A systematic review of randomized controlled trials. \emph{Intensive Care Medicine}, \bold{33}(12), 2058--2068. \verb{https://doi.org/10.1007/s00134-007-0897-3} } \references{ Stijnen, T., Hamza, T. H., & Ozdemir, P. (2010). Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. \emph{Statistics in Medicine}, \bold{29}(29), 3046--3067. \verb{https://doi.org/10.1002/sim.4040} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.nielweise2007 dat \dontrun{ ### load metafor package library(metafor) ### standard (inverse-variance) random-effects model res <- rma(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, drop00=TRUE) print(res, digits=3) predict(res, transf=exp, digits=2) ### random-effects conditional logistic model res <- rma.glmm(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, model="CM.EL") print(res, digits=3) predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{generalized linear models} \section{Concepts}{ medicine, odds ratios, generalized linear models } metadat/man/dat.viechtbauer2021.Rd0000644000176200001440000002205414750466700016324 0ustar liggesusers\name{dat.viechtbauer2021} \docType{data} \alias{dat.viechtbauer2021} \title{Studies to Illustrate Model Checking Methods} \description{Results from 20 hypothetical randomized clinical trials examining the effectiveness of a medication for treating some disease.} \usage{ dat.viechtbauer2021 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{numeric} \tab trial number \cr \bold{nTi} \tab \code{numeric} \tab number of patients in the treatment group \cr \bold{nCi} \tab \code{numeric} \tab number of patients in the control group \cr \bold{xTi} \tab \code{numeric} \tab number of patients in the treatment group with remission \cr \bold{xCi} \tab \code{numeric} \tab number of patients in the control group with remission \cr \bold{dose} \tab \code{numeric} \tab dosage of the medication provided to patients in the treatment group (in milligrams per day) } } \details{ The dataset was constructed for the purposes of illustrating the model checking and diagnostic methods described in Viechtbauer (2021). The code below provides the results for many of the analyses and plots discussed in the book chapter. } \source{ Viechtbauer, W. (2021). Model checking in meta-analysis. In C. H. Schmid, T. Stijnen, & I. R. White (Eds.), \emph{Handbook of meta-analysis} (pp. 219-254). Boca Raton, FL: CRC Press. \verb{https://doi.org/10.1201/9781315119403} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.viechtbauer2021 dat \dontrun{ ### load metafor package library(metafor) ### calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=xTi, n1i=nTi, ci=xCi, n2i=nCi, add=1/2, to="all", data=dat) dat ### number of studies k <- nrow(dat) ### fit models res.CE <- rma(yi, vi, data=dat, method="CE") # same as method="EE" res.CE res.RE <- rma(yi, vi, data=dat, method="DL") res.RE res.MR <- rma(yi, vi, mods = ~ dose, data=dat, method="FE") res.MR res.ME <- rma(yi, vi, mods = ~ dose, data=dat, method="DL") res.ME ### forest and bubble plot par(mar=c(5,4,1,2)) forest(dat$yi, dat$vi, psize=0.8, efac=0, xlim=c(-4,6), ylim=c(-3,23), cex=1, width=c(5,5,5), xlab="Log Odds Ratio (LnOR)", header=c("Trial", "LnOR [95\% CI]")) addpoly(res.CE, row=-1, mlab="CE Model") addpoly(res.RE, row=-2, mlab="RE Model") abline(h=0) tmp <- regplot(res.ME, xlim=c(0,250), ylim=c(-1,1.5), predlim=c(0,250), shade=FALSE, digits=1, xlab="Dosage (mg per day)", psize="seinv", plim=c(NA,5), bty="l", las=1, lty=c("solid", "dashed"), label=TRUE, labsize=0.8, offset=c(1,0.7)) res.sub <- rma(yi, vi, mods = ~ dose, data=dat, method="DL", subset=-6) abline(res.sub, lty="dotted") points(tmp$xi, tmp$yi, pch=21, cex=tmp$psize, col="black", bg="darkgray") par(mar=c(5,4,4,2)) ### number of standardized deleted residuals larger than +-1.96 in each model sum(abs(rstudent(res.CE)$z) >= qnorm(0.975)) sum(abs(rstudent(res.MR)$z) >= qnorm(0.975)) sum(abs(rstudent(res.RE)$z) >= qnorm(0.975)) sum(abs(rstudent(res.ME)$z) >= qnorm(0.975)) ### plot of the standardized deleted residuals for the RE and ME models plot(NA, NA, xlim=c(1,20), ylim=c(-4,4), xlab="Study", ylab="Standardized (Deleted) Residual", xaxt="n", main="Random-Effects Model", las=1) axis(side=1, at=1:20) abline(h=c(-1.96,1.96), lty="dotted") abline(h=0) points(1:20, rstandard(res.RE)$z, type="o", pch=19, col="gray70") points(1:20, rstudent(res.RE)$z, type="o", pch=19) legend("top", pch=19, col=c("gray70","black"), lty="solid", legend=c("Standardized Residuals","Standardized Deleted Residuals"), bty="n") plot(NA, NA, xlim=c(1,20), ylim=c(-4,4), xlab="Study", ylab="Standardized (Deleted) Residual", xaxt="n", main="Mixed-Effects Model", las=1) axis(side=1, at=1:20) abline(h=c(-1.96,1.96), lty="dotted") abline(h=0) points(1:20, rstandard(res.ME)$z, type="o", pch=19, col="gray70") points(1:20, rstudent(res.ME)$z, type="o", pch=19) legend("top", pch=19, col=c("gray70","black"), lty="solid", legend=c("Standardized Residuals","Standardized Deleted Residuals"), bty="n") ### Baujat plots baujat(res.CE, main="Common-Effects Model", xlab="Squared Pearson Residual", ylim=c(0,5), las=1) baujat(res.ME, main="Mixed-Effects Model", ylim=c(0,2), las=1) ### GOSH plots (skipped because this takes quite some time to run) if (FALSE) { res.GOSH.CE <- gosh(res.CE, subsets=10^7) plot(res.GOSH.CE, cex=0.2, out=6, xlim=c(-0.25,1.25), breaks=c(200,100)) res.GOSH.ME <- gosh(res.ME, subsets=10^7) plot(res.GOSH.ME, het="tau2", out=6, breaks=50, adjust=0.6, las=1) } ### plot of treatment dosage against the standardized residuals plot(dat$dose, rstandard(res.ME)$z, pch=19, xlab="Dosage (mg per day)", ylab="Standardized Residual", xlim=c(0,250), ylim=c(-2.5,2.5), las=1) abline(h=c(-1.96,1.96), lty="dotted", lwd=2) abline(h=0) title("Standardized Residual Plot") text(dat$dose[6], rstandard(res.ME)$z[6], "6", pos=4, offset=0.4) ### quadratic polynomial model rma(yi, vi, mods = ~ dose + I(dose^2), data=dat, method="DL") ### lack-of-fit model resLOF <- rma(yi, vi, mods = ~ dose + factor(dose), data=dat, method="DL", btt=3:9) resLOF ### scatter plot to illustrate the lack-of-fit model regplot(res.ME, xlim=c(0,250), ylim=c(-1.0,1.5), xlab="Dosage (mg per day)", ci=FALSE, predlim=c(0,250), psize=1, pch=19, col="gray60", digits=1, lwd=1, bty="l", las=1) dosages <- sort(unique(dat$dose)) lines(dosages, fitted(resLOF)[match(dosages, dat$dose)], type="o", pch=19, cex=2, lwd=2) points(dat$dose, dat$yi, pch=19, col="gray60") legend("bottomright", legend=c("Linear Model", "Lack-of-Fit Model"), pch=c(NA,19), col="black", lty="solid", lwd=c(1,2), pt.cex=c(1,2), seg.len=4, bty="n") ### checking normality of the standardized deleted residuals qqnorm(res.ME, type="rstudent", main="Standardized Deleted Residuals", pch=19, label="out", lwd=2, pos=24, ylim=c(-4,3), lty=c("solid", "dotted"), las=1) ### checking normality of the random effects sav <- qqnorm(ranef(res.ME)$pred, main="BLUPs of the Random Effects", cex=1, pch=19, xlim=c(-2.2,2.2), ylim=c(-0.6,0.6), las=1) abline(a=0, b=sd(ranef(res.ME)$pred), lwd=2) text(sav$x[6], sav$y[6], "6", pos=4, offset=0.4) ### hat values for the CE and RE models plot(NA, NA, xlim=c(1,20), ylim=c(0,0.21), xaxt="n", las=1, xlab="Study", ylab="Hat Value") axis(1, 1:20, cex.axis=1) points(hatvalues(res.CE), type="o", pch=19, col="gray70") points(hatvalues(res.RE), type="o", pch=19) abline(h=1/20, lty="dotted", lwd=2) title("Hat Values for the CE/RE Models") legend("topright", pch=19, col=c("gray70","black"), lty="solid", legend=c("Common-Effects Model", "Random-Effects Model"), bty="n") ### heatmap of the hat matrix for the ME model cols <- colorRampPalette(c("blue", "white", "red"))(101) h <- hatvalues(res.ME, type="matrix") image(1:nrow(h), 1:ncol(h), t(h[nrow(h):1,]), axes=FALSE, xlab="Influence of the Observed Effect of Study ...", ylab="On the Fitted Value of Study ...", col=cols, zlim=c(-max(abs(h)),max(abs(h)))) axis(1, 1:20, tick=FALSE) axis(2, 1:20, labels=20:1, las=1, tick=FALSE) abline(h=seq(0.5,20.5,by=1), col="white") abline(v=seq(0.5,20.5,by=1), col="white") points(1:20, 20:1, pch=19, cex=0.4) title("Heatmap for the Mixed-Effects Model") ### plot of leverages versus standardized residuals for the ME model plot(hatvalues(res.ME), rstudent(res.ME)$z, pch=19, cex=0.2+3*sqrt(cooks.distance(res.ME)), las=1, xlab="Leverage (Hat Value)", ylab="Standardized Deleted Residual", xlim=c(0,0.35), ylim=c(-3.5,2.5)) abline(h=c(-1.96,1.96), lty="dotted", lwd=2) abline(h=0, lwd=2) ids <- c(3,6,9) text(hatvalues(res.ME)[ids] + c(0,0.013,0.010), rstudent(res.ME)$z[ids] - c(0.18,0,0), ids) title("Leverage vs. Standardized Deleted Residuals") ### plot of the Cook's distances for the ME model plot(1:20, cooks.distance(res.ME), ylim=c(0,1.6), type="o", pch=19, las=1, xaxt="n", yaxt="n", xlab="Study", ylab="Cook's Distance") axis(1, 1:20, cex.axis=1) axis(2, seq(0,1.6,by=0.4), las=1) title("Cook's Distances") ### plot of the leave-one-out estimates of tau^2 for the ME model x <- influence(res.ME) plot(1:20, x$inf$tau2.del, ylim=c(0,0.15), type="o", pch=19, las=1, xaxt="n", xlab="Study", ylab=expression(paste("Estimate of ", tau^2, " without the ", italic(i), "th study"))) abline(h=res.ME$tau2, lty="dashed") axis(1, 1:20) title("Residual Heterogeneity Estimates") ### plot of the covariance ratios for the ME model plot(1:20, x$inf$cov.r, ylim=c(0,2.0), type="o", pch=19, las=1, xaxt="n", xlab="Study", ylab="Covariance Ratio") abline(h=1, lty="dashed") axis(1, 1:20) title("Covariance Ratios") ### fit mixed-effects model without studies 3 and/or 6 rma(yi, vi, mods = ~ dose, data=dat, method="DL", subset=-3) rma(yi, vi, mods = ~ dose, data=dat, method="DL", subset=-6) rma(yi, vi, mods = ~ dose, data=dat, method="DL", subset=-c(3,6)) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{outliers} \concept{model checks} \section{Concepts}{ medicine, odds ratios, outliers, model checks } metadat/man/dat.axfors2021.Rd0000644000176200001440000000663414750466700015333 0ustar liggesusers\name{dat.axfors2021} \docType{data} \alias{dat.axfors2021} \title{Mortality Outcomes with Hydroxychloroquine and Chloroquine in COVID-19 from an International Collaborative Meta-Analysis of Randomized Trials} \description{Results from 33 trials examining the effectiveness of hydroxychloroquine or chloroquine in patients with COVID-19.} \usage{ dat.axfors2021 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{character} \tab registry number \cr \bold{acronym} \tab \code{character} \tab shortened registry number \cr \bold{patient_setting} \tab \code{character} \tab patient setting \cr \bold{blinding_exact} \tab \code{character} \tab study blinding \cr \bold{high_dose} \tab \code{character} \tab high or low dose of medication \cr \bold{Published} \tab \code{character} \tab publication status \cr \bold{hcq_cq} \tab \code{character} \tab medication type (hcq = hydroxychloroquine or cq = chloroquine) \cr \bold{hcq_arm_event} \tab \code{numeric} \tab number of deaths in the treatment group \cr \bold{hcq_arm_total} \tab \code{numeric} \tab number of patients in the treatment group \cr \bold{control_arm_event} \tab \code{numeric} \tab number of deaths in the control group \cr \bold{control_arm_total} \tab \code{numeric} \tab number of patients in the control group \cr \bold{Control} \tab \code{character} \tab control group type (Standard of Care or Placebo) } } \details{ The dataset includes the results from 33 published and unpublished randomized clinical trials that examined the effectiveness of hydroxychloroquine or chloroquine in patients with COVID-19. The results given here are focused on the total mortality in the treatment versus control groups. } \references{ Axfors, C., Schmitt, A. M., Janiaud, P., van’t Hooft, J., Abd-Elsalam, S., Abdo, E. F., Abella, B. S., Akram, J., Amaravadi, R. K., Angus, D. C., Arabi, Y. M., Azhar, S., Baden, L. R., Baker, A. W., Belkhir, L., Benfield, T., Berrevoets, M. A. H., Chen, C.-P., Chen, T.-C., … Hemkens, L. G. (2021). Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials. Nature Communications, 12(1), 2349. \verb{https://doi.org/10.1038/s41467-021-22446-z} } \source{ Axfors, C., Schmitt, A., Janiaud, P., van ’t Hooft, J., Moher, D., Goodman, S., … Hemkens, L. G. (2021, March 9). Hydroxychloroquine and chloroquine for survival in COVID-19: An international collaborative meta-analysis of randomized trials. \verb{https://doi.org/10.17605/OSF.IO/QESV4} } \author{ W. Kyle Hamilton \email{whamilton@ucmerced.edu} \url{https://kylehamilton.com} } \examples{ # copy data into 'dat' and examine data dat <- dat.axfors2021 dat \dontrun{ # load metafor package library(metafor) # calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=hcq_arm_event, n1i=hcq_arm_total, ci=control_arm_event, n2i=control_arm_total, data=dat) # meta-analysis Hydroxychloroquine res_hcq <- rma(yi, vi, subset=(hcq_cq=="hcq"), slab = id, data=dat) print(res_hcq, digits=2) # meta-analysis Chloroquine res_cq <- rma(yi, vi, subset=(hcq_cq=="cq"), slab = id, data=dat) print(res_cq, digits=2) } } \keyword{datasets} \concept{medicine} \concept{covid-19} \concept{odds ratios} \section{Concepts}{ medicine, covid-19, odds ratios } metadat/man/dat.bassler2004.Rd0000644000176200001440000000601314750466700015454 0ustar liggesusers\name{dat.bassler2004} \docType{data} \alias{dat.bassler2004} \title{Studies on Ketotifen Alone or as Additional Medication for Long-Term Control of Asthma and Wheeze in Children} \description{Results from 10 trials reporting the physicians' judgement on the overall efficacy of ketotifen for long-term control of asthma and wheeze in children.} \usage{ dat.bassler2004 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study label \cr \bold{Ee} \tab \code{integer} \tab number of children with treament success (ketotifen group) \cr \bold{Ne} \tab \code{integer} \tab number of children (ketotifen group) \cr \bold{Ec} \tab \code{integer} \tab number of children with treament success (control group) \cr \bold{Nc} \tab \code{integer} \tab number of children (control group) \cr \bold{blind} \tab \code{character} \tab blinding of clinicians \cr } } \details{ Results from 10 trials reporting the physicians' judgement on the overall efficacy of Ketotifen for long-term control of asthma and wheeze in children. A prespecified subgroup analysis was conducted to evaluate whether the treatment effect is different in trials with adequate blinding compared to trials with inadequate / unclear blinding. This data set is used as an example in Schwarzer et al. (2015). } \source{ Bassler D., Mitra A. A. D., Ducharme F. M., Forster J., & Schwarzer, G. (2004). Ketotifen alone or as additional medication for long-term control of asthma and wheeze in children. \emph{Cochrane Database of Systematic Reviews}, \bold{1}, CD001384. \verb{https://doi.org/10.1002/14651858.CD001384.pub2} } \references{ Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). \emph{Meta-analysis with R}. Cham, Switzerland: Springer. } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show full data set dat.bassler2004 \dontrun{ ### Load meta package suppressPackageStartupMessages(library("meta")) ### Use DerSimonian-Laird estimator (which was the default in meta in the year 2015). ### Furthermore, print meta-analysis results with two digits. oldset <- settings.meta(method.tau = "DL", digits = 2) ### Calculate experimental and control event rates with(dat.bassler2004, summary(Ee / Ne)) with(dat.bassler2004, summary(Ec / Nc)) ### Conduct meta-analysis using the inverse variance method mb3 <- metabin(Ee, Ne, Ec, Nc, method = "I", data = dat.bassler2004, studlab = study) mb3 ### Conduct subgroup analysis comparing trials with adequate blinding ### to trials with inadequate or unclear blinding mb3s <- update(mb3, subgroup = blind, print.subgroup.name = FALSE) mb3s ### Conduct subgroup analysis assuming common between-study variance in subgroups mb3s.c <- update(mb3s, tau.common = TRUE) mb3s.c ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{risk ratios} \concept{medicine} \concept{subgroup analysis} \section{Concepts}{ risk ratios, medicine, subgroup analysis } metadat/man/dat.bangertdrowns2004.Rd0000644000176200001440000000717114750466700016706 0ustar liggesusers\name{dat.bangertdrowns2004} \docType{data} \alias{dat.bangertdrowns2004} \title{Studies on the Effectiveness of Writing-to-Learn Interventions} \description{Results from 48 studies on the effectiveness of school-based writing-to-learn interventions on academic achievement. \loadmathjax} \usage{ dat.bangertdrowns2004 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study number \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{grade} \tab \code{numeric} \tab grade level (1 = elementary; 2 = middle; 3 = high-school; 4 = college) \cr \bold{length} \tab \code{numeric} \tab treatment length (in weeks) \cr \bold{minutes} \tab \code{numeric} \tab minutes per assignment \cr \bold{wic} \tab \code{numeric} \tab writing tasks were completed in class (0 = no; 1 = yes) \cr \bold{feedback} \tab \code{numeric} \tab feedback on writing was provided (0 = no; 1 = yes) \cr \bold{info} \tab \code{numeric} \tab writing contained informational components (0 = no; 1 = yes) \cr \bold{pers} \tab \code{numeric} \tab writing contained personal components (0 = no; 1 = yes) \cr \bold{imag} \tab \code{numeric} \tab writing contained imaginative components (0 = no; 1 = yes) \cr \bold{meta} \tab \code{numeric} \tab prompts for metacognitive reflection (0 = no; 1 = yes) \cr \bold{subject} \tab \code{character} \tab subject matter \cr \bold{ni} \tab \code{numeric} \tab total sample size of the study \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance } } \details{ In each of the studies included in this meta-analysis, an experimental group (i.e., a group of students that received instruction with increased emphasis on writing tasks) was compared against a control group (i.e., a group of students that received conventional instruction) with respect to some content-related measure of academic achievement (e.g., final grade, an exam/quiz/test score). The outcome measure for this meta-analysis was the standardized mean difference (with positive values indicating a higher mean level of academic achievement in the intervention group). The standardized mean differences given here are bias-corrected and therefore differ slightly from the values reported in the article. Also, since only the total sample size is given in the article, the sampling variances were computed under the assumption that \mjeqn{n_{i1} = n_{i2} = n_i / 2}{n_i1 = n_i2 = n_i / 2}. } \source{ Bangert-Drowns, R. L., Hurley, M. M., & Wilkinson, B. (2004). The effects of school-based writing-to-learn interventions on academic achievement: A meta-analysis. \emph{Review of Educational Research}, \bold{74}(1), 29--58. \verb{https://doi.org/10.3102/00346543074001029} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bangertdrowns2004 dat[1:10,-13] \dontrun{ ### load metafor package library(metafor) ### fit random-effects model res <- rma(yi, vi, data=dat) res ### some examples of mixed-effects meta-regression models res <- rma(yi, vi, mods = ~ factor(grade), data=dat) res res <- rma(yi, vi, mods = ~ length, data=dat) res res <- rma(yi, vi, mods = ~ info + pers + imag + meta, data=dat) res } } \keyword{datasets} \concept{education} \concept{standardized mean differences} \concept{meta-regression} \section{Concepts}{ education, standardized mean differences, meta-regression } metadat/man/dat.li2007.Rd0000644000176200001440000000464714750466700014443 0ustar liggesusers\name{dat.li2007} \docType{data} \alias{dat.li2007} \title{Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction} \description{Results from 22 trials examining the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction.} \usage{ dat.li2007 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab trial id number \cr \bold{study} \tab \code{character} \tab first author or trial name \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{ai} \tab \code{numeric} \tab number of deaths in the magnesium group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in the magnesium group \cr \bold{ci} \tab \code{numeric} \tab number of deaths in the control group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in the control group } } \details{ The dataset includes the results from 22 randomized clinical trials that examined the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction. It is similar to the dataset \code{\link{dat.egger2001}}, with some slight differences in the included trials and data used. } \source{ Li, J., Zhang, Q., Zhang, M., & Egger, M. (2007). Intravenous magnesium for acute myocardial infarction. \emph{Cochrane Database of Systematic Reviews}, \bold{2}, CD002755. \verb{https://doi.org/10.1002/14651858.CD002755.pub2} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \seealso{ \code{\link{dat.egger2001}} } \examples{ ### copy data into 'dat' and examine data dat <- dat.li2007 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of all trials except ISIS-4 res <- rma(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, method="EE", subset=-14) print(res, digits=2) predict(res, transf=exp, digits=2) ### meta-analysis of all trials including ISIS-4 res <- rma(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, method="EE") print(res, digits=2) predict(res, transf=exp, digits=2) ### contour-enhanced funnel plot centered at 0 funnel(res, refline=0, level=c(90, 95, 99), shade=c("white", "gray", "darkgray")) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{odds ratios} \concept{publication bias} \section{Concepts}{ medicine, cardiology, odds ratios, publication bias } metadat/man/dat.collins1985b.Rd0000644000176200001440000000701014750466700015645 0ustar liggesusers\name{dat.collins1985b} \docType{data} \alias{dat.collins1985b} \title{Studies on the Effects of Diuretics in Pregnancy} \description{Results from 9 studies examining the effects of diuretics in pregnancy on various outcomes.} \usage{ dat.collins1985b } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study number \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{pre.nti} \tab \code{numeric} \tab number of women in treatment group followed up for pre-eclampsia outcome \cr \bold{pre.nci} \tab \code{numeric} \tab number of women in control/placebo group followed up for pre-eclampsia outcome \cr \bold{pre.xti} \tab \code{numeric} \tab number of women in treatment group with any form of pre-eclampsia \cr \bold{pre.xci} \tab \code{numeric} \tab number of women in control/placebo group with any form of pre-eclampsia \cr \bold{oedema} \tab \code{numeric} \tab dummy variable indicating whether oedema was a diagnostic criterion \cr \bold{fup.nti} \tab \code{numeric} \tab number of women in treatment group followed up for mortality outcomes \cr \bold{fup.nci} \tab \code{numeric} \tab number of women in control/placebo group followed up for mortality outcomes \cr \bold{ped.xti} \tab \code{numeric} \tab number of perinatal deaths in treatment group \cr \bold{ped.xci} \tab \code{numeric} \tab number of perinatal deaths in control/placebo group \cr \bold{stb.xti} \tab \code{numeric} \tab number of stillbirths in treatment group \cr \bold{stb.xci} \tab \code{numeric} \tab number of stillbirths in control/placebo group \cr \bold{ned.xti} \tab \code{numeric} \tab number of neonatal deaths in treatment group \cr \bold{ned.xci} \tab \code{numeric} \tab number of neonatal deaths in control/placebo group } } \details{ The 9 studies in this dataset examined the effects of diuretics in pregnancy on various outcomes, including the presence of any form of pre-eclampsia, perinatal death, stillbirth, and neonatal death. } \source{ Collins, R., Yusuf, S., & Peto, R. (1985). Overview of randomised trials of diuretics in pregnancy. \emph{British Medical Journal}, \bold{290}(6461), 17--23. \verb{https://doi.org/10.1136/bmj.290.6461.17} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.collins1985b dat \dontrun{ ### load metafor package library(metafor) ### calculate (log) odds ratio and sampling variance dat <- escalc(measure="OR", n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat) summary(dat, digits=2, transf=exp) ### meta-analysis using Peto's method for any form of pre-eclampsia rma.peto(n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat, digits=2) ### meta-analysis including only studies where oedema was not a diagnostic criterion rma.peto(n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat, digits=2, subset=(oedema==0)) ### meta-analyses of mortality outcomes (perinatal deaths, stillbirths, and neonatal deaths) rma.peto(n1i=fup.nti, n2i=fup.nci, ai=ped.xti, ci=ped.xci, data=dat, digits=2) rma.peto(n1i=fup.nti, n2i=fup.nci, ai=stb.xti, ci=stb.xci, data=dat, digits=2) rma.peto(n1i=fup.nti, n2i=fup.nci, ai=ned.xti, ci=ned.xci, data=dat, digits=2) } } \keyword{datasets} \concept{medicine} \concept{obstetrics} \concept{odds ratios} \concept{Peto's method} \section{Concepts}{ medicine, obstetrics, odds ratios, Peto's method } metadat/man/dat.bonett2010.Rd0000644000176200001440000000631114750466700015312 0ustar liggesusers\name{dat.bonett2010} \docType{data} \alias{dat.bonett2010} \title{Studies on the Reliability of the CES-D Scale} \description{Results from 9 studies on the reliability of the Center for Epidemiologic Studies Depression (CES-D) Scale administered to children providing care to an elderly parent.} \usage{ dat.bonett2010 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{source} \tab \code{character} \tab source of data \cr \bold{ni} \tab \code{numeric} \tab sample size \cr \bold{mi} \tab \code{numeric} \tab number of items in the scale \cr \bold{ai} \tab \code{numeric} \tab observed value of Cronbach's alpha \cr \bold{caregivers} \tab \code{character} \tab gender of the children in the sample } } \details{ The Center for Epidemiologic Studies Depression (CES-D) Scale is a 20-item questionnaire assessing various symptoms of depression, with each item scored on a 4-point scale. The scale has been used in several studies to examine depressive symptoms in children providing care to an elderly parent. The dataset includes information on the reliability of the scale as measured with Cronbach's alpha in 9 such studies. Also, the gender composition of the children in each sample is indicated. } \source{ Bonett, D. G. (2010). Varying coefficient meta-analytic methods for alpha reliability. \emph{Psychological Methods}, \bold{15}(4), 368--385. \verb{https://doi.org/10.1037/a0020142} } \references{ Bonett, D. G. (2002). Sample size requirements for testing and estimating coefficient alpha. \emph{Journal of Educational and Behavioral Statistics}, \bold{27}(4), 335--340. \verb{https://doi.org/10.3102/10769986027004335} Hakstian, A. R., & Whalen, T. E. (1976). A k-sample significance test for independent alpha coefficients. \emph{Psychometrika}, \bold{41}(2), 219--231. \verb{https://doi.org/10.1007/BF02291840} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.bonett2010 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis using the raw alpha values res <- rma(measure="ARAW", ai=ai, mi=mi, ni=ni, data=dat) res ### meta-analysis using transformed alpha values (using the ### transformation suggested by Hakstian & Whalen, 1976) res <- rma(measure="AHW", ai=ai, mi=mi, ni=ni, data=dat) res predict(res, transf=transf.iahw) ### meta-analysis using transformed alpha values (using the ### transformation suggested by Bonett, 2002) res <- rma(measure="ABT", ai=ai, mi=mi, ni=ni, data=dat) res predict(res, transf=transf.iabt) ### forest plot forest(res, slab=source, xlim=c(0,4.5), atransf=transf.iabt, refline=coef(res)) ### examine whether female/mixed samples yield different alphas (with raw alphas) res <- rma(measure="ARAW", ai=ai, mi=mi, ni=ni, mods = ~ caregivers, data=dat) res predict(res, newmods=c(0,1), digits=2) } } \keyword{datasets} \concept{psychology} \concept{Cronbach's alpha} \concept{reliability generalization} \concept{meta-regression} \section{Concepts}{ psychology, Cronbach's alpha, reliability generalization, meta-regression } metadat/man/dat.raudenbush1985.Rd0000644000176200001440000001136214750466700016205 0ustar liggesusers\name{dat.raudenbush1985} \docType{data} \alias{dat.raudenbush1985} \title{Studies on Assessing the Effects of Teacher Expectations on Pupil IQ} \description{Results from 19 studies examining how teachers' expectations about their pupils can influence actual IQ levels.} \usage{ dat.raudenbush1985 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{author} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{weeks} \tab \code{numeric} \tab weeks of contact prior to expectancy induction \cr \bold{setting} \tab \code{character} \tab whether tests were group or individually administered \cr \bold{tester} \tab \code{character} \tab whether test administrator was aware or blind \cr \bold{n1i} \tab \code{numeric} \tab sample size of experimental group \cr \bold{n2i} \tab \code{numeric} \tab sample size of control group \cr \bold{yi} \tab \code{numeric} \tab standardized mean difference \cr \bold{vi} \tab \code{numeric} \tab corresponding sampling variance } } \details{ In the so-called \sQuote{Pygmalion study} (Rosenthal & Jacobson, 1968), \dQuote{all of the predominantly poor children in the so-called Oak elementary school were administered a test pretentiously labeled the \sQuote{Harvard Test of Inflected Acquisition.} After explaining that this newly designed instrument had identified those children most likely to show dramatic intellectual growth during the coming year, the experimenters gave the names of these \sQuote{bloomers} to the teachers. In truth, the test was a traditional IQ test and the \sQuote{bloomers} were a randomly selected 20\% of the student population. After retesting the children 8 months later, the experimenters reported that those predicted to bloom had in fact gained significantly more in total IQ (nearly 4 points) and reasoning IQ (7 points) than the control group children. Further, at the end of the study, the teachers rated the experimental children as intellectually more curious, happier, better adjusted, and less in need of approval than their control group peers} (Raudenbush, 1984). In the following years, a series of studies were conducted attempting to replicate this rather controversial finding. However, the great majority of those studies were unable to demonstrate a statistically significant difference between the two experimental groups in terms of IQ scores. Raudenbush (1984) conducted a meta-analysis based on 19 such studies to further examine the evidence for the existence of the \sQuote{Pygmalion effect}. The dataset includes the results from these studies. The outcome measure used for the meta-analysis was the standardized mean difference (\code{yi}), with positive values indicating that the supposed \sQuote{bloomers} had, on average, higher IQ scores than those in the control group. The \code{weeks} variable indicates the number of weeks of prior contact between teachers and students before the expectancy induction. Testing was done either in a group setting or individually, which is indicated by the \code{setting} variable. Finally, the \code{tester} variable indicates whether the test administrators were either aware or blind to the researcher-provided designations of the children's intellectual potential. The data in this dataset were obtained from Raudenbush and Bryk (1985) with information on the \code{setting} and \code{tester} variables extracted from Raudenbush (1984). } \source{ Raudenbush, S. W. (1984). Magnitude of teacher expectancy effects on pupil IQ as a function of the credibility of expectancy induction: A synthesis of findings from 18 experiments. \emph{Journal of Educational Psychology}, \bold{76}(1), 85--97. \verb{https://doi.org/10.1037/0022-0663.76.1.85} Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. \emph{Journal of Educational Statistics}, \bold{10}(2), 75--98. \verb{https://doi.org/10.3102/10769986010002075} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.raudenbush1985 dat \dontrun{ ### load metafor package library(metafor) ### random-effects model res <- rma(yi, vi, data = dat) res ### create weeks variable where values larger than 3 are set to 3 dat$weeks.c <- ifelse(dat$weeks > 3, 3, dat$weeks) ### mixed-effects model with weeks.c variable as moderator res <- rma(yi, vi, mods = ~ weeks.c, data = dat, digits = 3) res } } \keyword{datasets} \concept{education} \concept{standardized mean differences} \concept{meta-regression} \section{Concepts}{ education, standardized mean differences, meta-regression } metadat/man/dat.linde2005.Rd0000644000176200001440000001112414750466700015114 0ustar liggesusers\name{dat.linde2005} \docType{data} \alias{dat.linde2005} \title{Studies on the Effectiveness of St. John's Wort for Treating Depression} \description{Results from 26 studies on the effectiveness of Hypericum perforatum extracts (St. John's wort) for treating depression.} \usage{ dat.linde2005 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study number \cr \bold{study} \tab \code{character} \tab study author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{country} \tab \code{character} \tab study location \cr \bold{ni} \tab \code{numeric} \tab total sample size \cr \bold{major} \tab \code{numeric} \tab sample restricted to patients who met criteria for major depression \cr \bold{baseline} \tab \code{numeric} \tab HRSD baseline score \cr \bold{version} \tab \code{numeric} \tab HRSD version (17 or 21 items) \cr \bold{duration} \tab \code{numeric} \tab study duration (in weeks) \cr \bold{prep} \tab \code{character} \tab Hypericum extract preparation \cr \bold{dosage} \tab \code{numeric} \tab dosage (in mg) \cr \bold{response} \tab \code{numeric} \tab definition of response (see \sQuote{Details}) \cr \bold{ai} \tab \code{numeric} \tab number of responses in treatment group \cr \bold{n1i} \tab \code{numeric} \tab number of patients in treatment group \cr \bold{ci} \tab \code{numeric} \tab number of responses in placebo group \cr \bold{n2i} \tab \code{numeric} \tab number of patients in placebo group \cr \bold{group} \tab \code{numeric} \tab stratification variable used by the authors (see \sQuote{Details}) } } \details{ The dataset includes the results from 26 double-blind placebo-controlled trials on the effectiveness of Hypericum perforatum extracts (St. John's wort) for treating depression (note that 2 studies did not provide sufficient response information). Data were extracted from Table 1 and Figure 3 from Linde et al. (2005). For study duration, the assessment week (instead of the total study duration) was coded for Philipp et al. (1999) and Montgomery et al. (2000). For dosage, the midpoint was coded when a range of values was given. The definition of what constitutes a \code{response} differed across studies and is coded as follows: \enumerate{ \item HRSD score reduction of at least 50\% or HRSD score after therapy <10, \item HRSD reduction of at least 50\%, \item based on HRSD scale but exact definition not reported, \item global patient assessment of efficacy, \item at least \sQuote{much improved} on the Clinical Global Impression sub-scale for global improvement. } The \code{group} variable corresponds to the variable used by Linde et al. (2005) to stratify their analyses and is coded as follows: \enumerate{ \item smaller trials restricted to major depression, \item larger trials restricted to major depression, \item smaller trials not restricted to major depression, \item larger trials not restricted to major depression. } } \source{ Linde, K., Berner, M., Egger, M., & Mulrow, C. (2005). St John's wort for depression: Meta-analysis of randomised controlled trials. \emph{British Journal of Psychiatry}, \bold{186}(2), 99--107. \verb{https://doi.org/10.1192/bjp.186.2.99} } \references{ Viechtbauer, W. (2007). Accounting for heterogeneity via random-effects models and moderator analyses in meta-analysis. \emph{Zeitschrift \enc{für}{fuer} Psychologie / Journal of Psychology}, \bold{215}(2), 104--121. \verb{https://doi.org/10.1027/0044-3409.215.2.104} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.linde2005 head(dat) \dontrun{ ### load metafor package library(metafor) ### remove studies with no response information and study with no responses in either group dat <- dat[-c(5,6,26),] ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat) head(dat) ### meta-analysis of the log risk ratios using a random-effects model res <- rma(yi, vi, data=dat, method="DL") res ### mixed-effects meta-regression model with stratification variable res <- rma(yi, vi, mods = ~ 0 + factor(group), data=dat, method="DL") res ### predicted average risk ratio for each level of the stratification variable predict(res, newmods=diag(4), transf=exp, digits=2) } } \keyword{datasets} \concept{medicine} \concept{psychiatry} \concept{risk ratios} \section{Concepts}{ medicine, psychiatry, risk ratios } metadat/man/dat.cohen1981.Rd0000644000176200001440000000514714750466700015141 0ustar liggesusers\name{dat.cohen1981} \docType{data} \alias{dat.cohen1981} \title{Studies on the Relationship between Course Instructor Ratings and Student Achievement} \description{Results from 20 studies on the correlation between course instructor ratings and student achievement.} \usage{ dat.cohen1981 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab study author(s) and year \cr \bold{sample} \tab \code{character} \tab course type \cr \bold{control} \tab \code{character} \tab ability control \cr \bold{ni} \tab \code{numeric} \tab sample size of the study (number of sections) \cr \bold{ri} \tab \code{numeric} \tab observed correlation } } \details{ The studies included in this dataset examined to what extent students' ratings of a course instructor correlated with their achievement in the course. Instead of correlating individual ratings and achievement scores, the studies were carried out in multisection courses, in which the sections had different instructors but all sections used a common achievement measure (e.g., a final exam). The correlation coefficients reflect the correlation between the mean instructor rating and the mean achievement score of each section. Hence, the unit of analysis are the sections, not the individuals. Note that this dataset (extracted from Table A.3 in Cooper & Hedges, 1994) only contains studies with at least 10 sections. } \source{ Cooper, H., & Hedges, L. V. (1994). Appendix A: Data Sets. In H. Cooper & L. V. Hedges (Eds.), \emph{The handbook of research synthesis} (pp. 543--547). New York: Russell Sage Foundation. } \references{ Cohen, P. A. (1981). Student ratings of instruction and student achievement: A meta-analysis of multisection validity studies. \emph{Review of Educational Research}, \bold{51}(3), 281--309. \verb{https://doi.org/10.3102/00346543051003281} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.cohen1981 dat[c(1,4,5)] \dontrun{ ### load metafor package library(metafor) ### calculate r-to-z transformed correlations and corresponding sampling variances dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat[c(1,4,5)]) dat ### meta-analysis of the transformed correlations using a random-effects model res <- rma(yi, vi, data=dat, digits=2) res ### predicted average correlation with 95\% CI predict(res, transf=transf.ztor) } } \keyword{datasets} \concept{education} \concept{correlation coefficients} \section{Concepts}{ education, correlation coefficients } metadat/man/dat.colditz1994.Rd0000644000176200001440000001023714750466700015515 0ustar liggesusers\name{dat.colditz1994} \docType{data} \alias{dat.colditz1994} \title{Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis} \description{Results from 13 studies examining the effectiveness of the Bacillus Calmette-Guerin (BCG) vaccine against tuberculosis. \loadmathjax} \usage{ dat.colditz1994 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{numeric} \tab trial number \cr \bold{author} \tab \code{character} \tab author(s) \cr \bold{year} \tab \code{numeric} \tab publication year \cr \bold{tpos} \tab \code{numeric} \tab number of TB positive cases in the treated (vaccinated) group \cr \bold{tneg} \tab \code{numeric} \tab number of TB negative cases in the treated (vaccinated) group \cr \bold{cpos} \tab \code{numeric} \tab number of TB positive cases in the control (non-vaccinated) group \cr \bold{cneg} \tab \code{numeric} \tab number of TB negative cases in the control (non-vaccinated) group \cr \bold{ablat} \tab \code{numeric} \tab absolute latitude of the study location (in degrees) \cr \bold{alloc} \tab \code{character} \tab method of treatment allocation (random, alternate, or systematic assignment) } } \details{ The 13 studies provide data in terms of \mjeqn{2 \times 2}{2x2} tables in the form: \tabular{lcc}{ \tab TB positive \tab TB negative \cr vaccinated group \tab \code{tpos} \tab \code{tneg} \cr control group \tab \code{cpos} \tab \code{cneg} } The goal of the meta-analysis was to examine the overall effectiveness of the BCG vaccine for preventing tuberculosis and to examine moderators that may potentially influence the size of the effect. The dataset has been used in several publications to illustrate meta-analytic methods (see \sQuote{References}). } \source{ Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., & Mosteller, F. (1994). Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. \emph{Journal of the American Medical Association}, \bold{271}(9), 698--702. \verb{https://doi.org/10.1001/jama.1994.03510330076038} } \references{ Berkey, C. S., Hoaglin, D. C., Mosteller, F., & Colditz, G. A. (1995). A random-effects regression model for meta-analysis. \emph{Statistics in Medicine}, \bold{14}(4), 395--411. \verb{https://doi.org/10.1002/sim.4780140406} van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. \emph{Statistics in Medicine}, \bold{21}(4), 589--624. \verb{https://doi.org/10.1002/sim.1040} Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. \emph{Journal of Statistical Software}, \bold{36}(3), 1--48. \verb{https://doi.org/10.18637/jss.v036.i03} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.colditz1994 dat \dontrun{ ### load metafor package library(metafor) ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat, slab=paste0(author, ", ", year)) dat ### random-effects model res <- rma(yi, vi, data=dat) res ### average risk ratio with 95\% CI predict(res, transf=exp) ### mixed-effects model with absolute latitude and publication year as moderators res <- rma(yi, vi, mods = ~ ablat + year, data=dat) res ### predicted average risk ratios for 10-60 degrees absolute latitude ### holding the publication year constant at 1970 predict(res, newmods=cbind(seq(from=10, to=60, by=10), 1970), transf=exp) ### note: the interpretation of the results is difficult because absolute ### latitude and publication year are strongly correlated (the more recent ### studies were conducted closer to the equator) plot(ablat ~ year, data=dat, pch=19, xlab="Publication Year", ylab="Absolute Lattitude") cor(dat$ablat, dat$year) } } \keyword{datasets} \concept{medicine} \concept{risk ratios} \concept{meta-regression} \section{Concepts}{ medicine, risk ratios, meta-regression } metadat/man/dat.woods2010.Rd0000644000176200001440000000424614750466700015157 0ustar liggesusers\name{dat.woods2010} \docType{data} \alias{dat.woods2010} \title{Studies on Treatments for Chronic Obstructive Pulmonary Disease} \description{Results from 3 trials examining the mortality risk of three treatments and placebo in patients with chronic obstructive pulmonary disease.} \usage{ dat.woods2010 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab first author / study name \cr \bold{treatment} \tab \code{character} \tab treatment \cr \bold{r} \tab \code{integer} \tab number of deaths \cr \bold{N} \tab \code{integer} \tab number of patients } } \details{ Count mortality statistics in randomised controlled trials of treatments for chronic obstructive pulmonary disease (Woods et al., 2010, Table 1). } \source{ Woods, B. S., Hawkins, N., & Scott, D. A. (2010). Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial. \emph{BMC Medical Research Methodology}, \bold{10}, 54. \verb{https://doi.org/10.1186/1471-2288-10-54} } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show full dataset dat.woods2010 \dontrun{ ### Load netmeta package suppressPackageStartupMessages(library("netmeta")) ### Print odds ratios and confidence limits with two digits oldset <- settings.meta(digits = 2) ### Change appearance of confidence intervals cilayout("(", "-") ### Transform data from long arm-based format to contrast-based ### format. Argument 'sm' has to be used for odds ratio as summary ### measure; by default the risk ratio is used in the metabin function ### called internally. pw <- pairwise(treatment, event = r, n = N, studlab = author, data = dat.woods2010, sm = "OR") pw ### Conduct network meta-analysis net <- netmeta(pw) net ### Show forest plot forest(net, ref = "Placebo", drop = TRUE, leftlabs = "Contrast to Placebo") ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{network meta-analysis} \section{Concepts}{ medicine, odds ratios, network meta-analysis } metadat/man/dat.curtis1998.Rd0000644000176200001440000001311614750466700015361 0ustar liggesusers\name{dat.curtis1998} \docType{data} \alias{dat.curtis1998} \title{Studies on the Effects of Elevated CO2 Levels on Woody Plant Mass} \description{Results from studies examining the effects of elevated CO2 levels on woody plant mass.} \usage{ dat.curtis1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab observation number \cr \bold{paper} \tab \code{numeric} \tab paper number \cr \bold{genus} \tab \code{character} \tab genus name \cr \bold{species} \tab \code{character} \tab species name \cr \bold{fungrp} \tab \code{character} \tab plant functional group \cr \bold{co2.ambi} \tab \code{numeric} \tab ambient CO2 level (control group) \cr \bold{co2.elev} \tab \code{numeric} \tab elevated CO2 level (treatment group) \cr \bold{units} \tab \code{character} \tab units for CO2 exposure levels \cr \bold{time} \tab \code{numeric} \tab maximum length of time (days) of CO2 exposure \cr \bold{pot} \tab \code{character} \tab growing method (see \sQuote{Details}) \cr \bold{method} \tab \code{character} \tab CO2 exposure facility (see \sQuote{Details}) \cr \bold{stock} \tab \code{character} \tab planting stock code \cr \bold{xtrt} \tab \code{character} \tab interacting treatment code (see \sQuote{Details}) \cr \bold{level} \tab \code{character} \tab interacting treatment level codes (see \sQuote{Details}) \cr \bold{m1i} \tab \code{numeric} \tab mean plant mass under elevated CO2 level (treatment group) \cr \bold{sd1i} \tab \code{numeric} \tab standard deviation of plant mass underelevated CO2 level (treatment group) \cr \bold{n1i} \tab \code{numeric} \tab number of observations under elevated CO2 level (treatment group) \cr \bold{m2i} \tab \code{numeric} \tab mean plant mass under ambient CO2 level (control group) \cr \bold{sd2i} \tab \code{numeric} \tab standard deviation of plant mass under ambient CO2 level (control group) \cr \bold{n2i} \tab \code{numeric} \tab number of observations under ambient CO2 level (control group) } } \details{ The studies included in this dataset compared the total above- plus below-ground biomass (in grams) for plants that were either exposed to ambient (around 35 Pa) and elevated CO2 levels (around twice the ambient level). The \code{co2.ambi} and \code{co2.elev} variables indicate the CO2 levels in the control and treatment groups, respectively (with the \code{units} variable specifying the units for the CO2 exposure levels). Many of the studies also varied one or more additional environmental variables (defined by the \code{xtrt} and \code{level} variables): \itemize{ \item NONE = no additional treatment factor \item FERT = soil fertility (either a \code{CONTROL}, \code{HIGH}, or \code{LOW} level) \item LIGHT = light treatment (always a \code{LOW} light level) \item FERT+L = soil fertility and light (a \code{LOW} light and soil fertility level) \item H2O = well watered vs drought (either a \code{WW} or \code{DRT} level) \item TEMP = temperature treatment (either a \code{HIGH} or \code{LOW} level) \item OZONE = ozone exposure (either a \code{HIGH} or \code{LOW} level) \item UVB = ultraviolet-B radiation exposure (either a \code{HIGH} or \code{LOW} level) } In addition, the studies differed with respect to various design variables, including CO2 exposure duration (\code{time}), growing method (\code{pot}: number = pot size in liters; \code{GRND} = plants rooted in ground; \code{HYDRO} = solution or aeroponic culture), CO2 exposure facility (\code{method}: \code{GC} = growth chamber; \code{GH} = greenhouse; \code{OTC} = field-based open-top chamber), and planting stock (\code{stock}: \code{SEED} = plants started from seeds; \code{SAP} = plants started from cuttings). The goal of the meta-analysis was to examine the effects of elevated CO2 levels on plant physiology and growth and the interacting effects of the environmental (and design) variables. } \source{ Hedges, L. V., Gurevitch, J., & Curtis, P. S. (1999). The meta-analysis of response ratios in experimental ecology. \emph{Ecology}, \bold{80}(4), 1150--1156. \verb{https://doi.org/10.1890/0012-9658(1999)080[1150:TMAORR]2.0.CO;2} (data obtained from \emph{Ecological Archives}, E080-008-S1, at: \verb{https://doi.org/10.6084/m9.figshare.c.3297278}) } \references{ Curtis, P. S., & Wang, X. (1998). A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. \emph{Oecologia}, \bold{113}(3), 299--313. \verb{https://doi.org/10.1007/s004420050381} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.curtis1998 head(dat) \dontrun{ ### load metafor package library(metafor) ### calculate (log transformed) ratios of means and corresponding sampling variances dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) head(dat) ### meta-analysis using a random-effects model res <- rma(yi, vi, method="DL", data=dat) res ### average ratio of means with 95\% CI predict(res, transf=exp, digits=2) ### meta-analysis for plants grown under nutrient stress res <- rma(yi, vi, method="DL", data=dat, subset=(xtrt=="FERT" & level=="LOW")) predict(res, transf=exp, digits=2) ### meta-analysis for plants grown under low light conditions res <- rma(yi, vi, method="DL", data=dat, subset=(xtrt=="LIGHT" & level=="LOW")) predict(res, transf=exp, digits=2) } } \keyword{datasets} \concept{ecology} \concept{ratios of means} \section{Concepts}{ ecology, ratios of means } metadat/man/dat.furukawa2003.Rd0000644000176200001440000000512214750466700015645 0ustar liggesusers\name{dat.furukawa2003} \docType{data} \alias{dat.furukawa2003} \title{Studies on Low Dosage Tricyclic Antidepressants for the Treatment of Depression} \description{Results on depression severity from 17 studies comparing low dosage tricyclic antidepressants (TCA) and placebo for the treatment of depression.} \usage{ dat.furukawa2003 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{author} \tab \code{character} \tab First author with information on dosage in parentheses \cr \bold{Ne} \tab \code{integer} \tab number of patients in low TCA group \cr \bold{Me} \tab \code{numeric} \tab depression severity (low TCA) \cr \bold{Se} \tab \code{numeric} \tab standard deviation (low TCA) \cr \bold{Nc} \tab \code{integer} \tab number of patients in placebo group \cr \bold{Mc} \tab \code{numeric} \tab depression severity (placebo) \cr \bold{Sc} \tab \code{numeric} \tab standard deviation (placebo) \cr } } \details{ Furukawa et al. (2003) carried out a systematic review comparing low dosage tricyclic antidepressants (TCA) with placebo for the treatment of depression. They reported the effect on presence/absence of depression and on depression severity at various time points. Here we focus on depression severity at four weeks. Most studies used some version of the Hamilton Depression Rating Scale, however, some studies used the Montgomery-Asberg Depression Rating Scale. Accordingly, it is not possible to pool the estimated effects directly. This data set is used as an example in Schwarzer et al. (2015). } \source{ Furukawa, T. A., McGuire, H., & Barbui, C. (2003). Low dosage tricyclic antidepressants for depression. \emph{Cochrane Database of Systematic Reviews}, \bold{3}, CD003197. \verb{https://doi.org/10.1002/14651858.CD003197} } \references{ Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). \emph{Meta-analysis with R}. Cham, Switzerland: Springer. } \author{ Guido Schwarzer, \email{guido.schwarzer@uniklinik-freiburg.de}, \url{https://github.com/guido-s/} } \examples{ ### Show first five studies head(dat.furukawa2003, 5) \dontrun{ ### Load meta package suppressPackageStartupMessages(library("meta")) ### Use RevMan5 settings oldset <- settings.meta("RevMan5", digits = 2) ### Conduct random effects meta-analysis with Hedges' g as effect measure mc2 <- metacont(Ne, Me, Se, Nc, Mc, Sc, common = FALSE, data = dat.furukawa2003, sm = "SMD") mc2 ### Use previous settings settings.meta(oldset) } } \keyword{datasets} \concept{standardized mean differences} \section{Concepts}{ standardized mean differences } metadat/man/dat.pignon2000.Rd0000644000176200001440000000603514750466700015313 0ustar liggesusers\name{dat.pignon2000} \docType{data} \alias{dat.pignon2000} \title{Studies on the Effectiveness of Locoregional Treatment plus Chemotherapy for Head and Neck Squamous-Cell Carcinoma} \description{Results from studies examining mortality risk in patients with nonmetastatic head and neck squamous-cell carcinoma receiving either locoregional treatment plus chemotherapy versus locoregional treatment alone.} \usage{ dat.pignon2000 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study id number \cr \bold{trial} \tab \code{character} \tab trial abbreviation \cr \bold{OmE} \tab \code{numeric} \tab observed minus expected number of deaths in the locoregional treatment plus chemotherapy group \cr \bold{V} \tab \code{numeric} \tab corresponding variance \cr \bold{grp} \tab \code{numeric} \tab timing of chemotherapy: 1 = adjuvant, 2 = neoadjuvant, 3 = concomitant } } \details{ The purpose of this meta-analysis was to examine the mortality risk in patients with nonmetastatic head and neck squamous-cell carcinoma receiving either locoregional treatment plus chemotherapy versus locoregional treatment alone. For 65 trials, the dataset provides the observed minus expected number of deaths and corresponding variances in the locoregional treatment plus chemotherapy group. Based on these values, we can estimate the log hazard ratios with \code{OmE/V} and the corresponding sampling variance with \code{1/V}. The trials were also divided according to the timing of the chomotherapy: (1) adjuvant, after the locoregional treatment, (2) neoadjuvant, before the locoregional treatment, and (3) concomitant, chemotherapy given concomitantly or alternating with radiotherapy. } \source{ Pignon, J. P., Bourhis, J., Domenge, C., & Designe, L. (2000). Chemotherapy added to locoregional treatment for head and neck squamous-cell carcinoma: Three meta-analyses of updated individual data. \emph{Lancet}, \bold{355}(9208), 949--955. \verb{https://doi.org/10.1016/S0140-6736(00)90011-4} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.pignon2000 head(dat) \dontrun{ ### load metafor package library(metafor) ### calculate log hazard ratios and sampling variances dat$yi <- with(dat, OmE/V) dat$vi <- with(dat, 1/V) head(dat) ### meta-analysis based on all 65 trials res <- rma(yi, vi, data=dat, method="EE", digits=2) res predict(res, transf=exp) ### only adjuvant trials res <- rma(yi, vi, data=dat, method="EE", subset=grp==1, digits=2) res predict(res, transf=exp) ### only neoadjuvant trials res <- rma(yi, vi, data=dat, method="EE", subset=grp==2, digits=2) res predict(res, transf=exp) ### only concomitant trials res <- rma(yi, vi, data=dat, method="EE", subset=grp==3, digits=2) res predict(res, transf=exp) } } \keyword{datasets} \concept{medicine} \concept{oncology} \concept{hazard ratios} \section{Concepts}{ medicine, oncology, hazard ratios } metadat/man/dat.dagostino1998.Rd0000644000176200001440000001226214750466700016040 0ustar liggesusers\name{dat.dagostino1998} \docType{data} \alias{dat.dagostino1998} \title{Studies on the Effectiveness of Antihistamines in Reducing Symptoms of the Common Cold} \description{Results from 9 studies on the effectiveness of antihistamines in reducing the severity of runny nose and sneezing in the common cold.} \usage{ dat.dagostino1998 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study id \cr \bold{cold} \tab \code{character} \tab natural or induced cold study \cr \bold{scale.rn} \tab \code{character} \tab scale for measuring runny nose severity \cr \bold{scale.sn} \tab \code{character} \tab scale for measuring sneezing severity \cr \bold{drug} \tab \code{character} \tab type of antihistamine studied \cr \bold{tnt} \tab \code{numeric} \tab total sample size of the treatment group \cr \bold{tnc} \tab \code{numeric} \tab total sample size of the control (placebo) group \cr \bold{outcome} \tab \code{character} \tab outcome variable (see \sQuote{Details})\cr \bold{mt} \tab \code{numeric} \tab mean in the treatment group \cr \bold{sdt} \tab \code{numeric} \tab SD in the treatment group \cr \bold{mc} \tab \code{numeric} \tab mean in the control group \cr \bold{sdc} \tab \code{numeric} \tab SD in the control group \cr \bold{xt} \tab \code{numeric} \tab number of patients reaching the therapy goal in the treatment group \cr \bold{xc} \tab \code{numeric} \tab number of patients reaching the therapy goal in the control (placebo) group \cr \bold{nt} \tab \code{numeric} \tab sample size of the treatment group for measuring the outcome \cr \bold{nc} \tab \code{numeric} \tab sample size of the control group for measuring the outcome } } \details{ The studies for this meta-analysis were assembled to examine the effectiveness of antihistamines in reducing the severity of runny nose and sneezing in the common cold. Effectiveness was measured after one and two days of treatment in terms of 4 different outcome variables: \enumerate{ \item \code{rnic1} and \code{rnic2} (continuous): incremental change (improvement) in runny nose severity at day 1 and day 2, \item \code{rngoal1} and \code{rngoal2} (dichotomous): reaching the goal of therapy (of at least a 50\% reduction in runny nose severity) at day 1 and day 2, \item \code{snic1} and \code{snic2} (continuous): incremental change (improvement) in sneezing severity at day 1 and day 2, and \item \code{rngoal1} and \code{rngoal2} (dichotomous): reaching the goal of therapy (of at least a 50\% reduction in sneezing severity) at day 1 and day 2. } For the continuous outcomes, standardized mean differences can be computed to quantify the difference between the treatment and control groups. For the dichotomous outcomes, one can compute (log) odds ratios to quantify the difference between the treatment and control groups. } \source{ D'Agostino, R. B., Sr., Weintraub, M., Russell, H. K., Stepanians, M., D'Agostino, R. B., Jr., Cantilena, L. R., Jr., Graumlich, J. F., Maldonado, S., Honig, P., & Anello, C. (1998). The effectiveness of antihistamines in reducing the severity of runny nose and sneezing: A meta-analysis. \emph{Clinical Pharmacology & Therapeutics}, \bold{64}(6), 579--596. \verb{https://doi.org/10.1016/S0009-9236(98)90049-2} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.dagostino1998 head(dat, 16) \dontrun{ ### load metafor package library(metafor) ### compute standardized mean differences and corresponding sampling variances dat <- escalc(measure="SMD", m1i=mt, m2i=mc, sd1i=sdt, sd2i=sdc, n1i=nt, n2i=nc, data=dat, add.measure=TRUE) ### compute log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=xt, ci=xc, n1i=nt, n2i=nc, data=dat, replace=FALSE, add.measure=TRUE, add=1/2, to="all") ### inspect data for the first study head(dat, 8) ### fit a random-effects model for incremental change in runny nose severity at day 1 res <- rma(yi, vi, data=dat, subset=outcome=="rnic1") res ### fit a random-effects model for reaching the goal of therapy for runny nose severity at day 1 res <- rma(yi, vi, data=dat, subset=outcome=="rngoal1") res predict(res, transf=exp) ### construct approximate V matrix assuming a correlation of 0.7 for sampling errors within studies dat$esid <- ave(dat$study, dat$study, FUN=seq) V <- vcalc(vi, cluster=study, obs=esid, rho=0.7, data=dat) ### fit a model for incremental change in runny nose severity at day 1 and at day 2, allowing for ### correlated sampling errors (no random effects added, since there does not appear to be any ### noteworthy heterogeneity in these data) res <- rma.mv(yi, V, mods = ~ 0 + outcome, data=dat, subset=outcome \%in\% c("rnic1","rnic2")) res ### test if there is a difference in effects at day 1 and day 2 anova(res, X=c(1,-1)) } } \keyword{datasets} \concept{medicine} \concept{standardized mean differences} \concept{odds ratios} \concept{multivariate models} \section{Concepts}{ medicine, standardized mean differences, odds ratios, multivariate models } metadat/man/dat.hahn2001.Rd0000644000176200001440000000476114750466700014744 0ustar liggesusers\name{dat.hahn2001} \docType{data} \alias{dat.hahn2001} \title{Studies on the Effectiveness of Different Rehydration Solutions for the Prevention of Unscheduled Intravenous Infusion in Children with Diarrhoea} \description{Results from 12 trials examining the effectiveness of a reduced versus standard rehydration solution for the prevention of unscheduled intravenous infusion in children with diarrhoea.} \usage{ dat.hahn2001 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{character} \tab trial name and year \cr \bold{ai} \tab \code{numeric} \tab number of children requiring unscheduled intravenous infusion in the reduced rehydration solution group \cr \bold{n1i} \tab \code{numeric} \tab number of children in the reduced rehydration solution group \cr \bold{ci} \tab \code{numeric} \tab number of children requiring unscheduled intravenous infusion in the standard rehydration solution group \cr \bold{n2i} \tab \code{numeric} \tab number of children in the standard rehydration solution group } } \details{ The dataset includes the results from 12 randomized clinical trials that examined the effectiveness of a reduced osmolarity oral rehydration solution (total osmolarity <250 mmol/l with reduced sodium) with a standard WHO oral rehydration solution (sodium 90 mmol/l, glucose 111mmol/l, total osmolarity 311 mmol/l) for the prevention of unscheduled intravenous infusion in children with diarrhoea. } \source{ Hahn, S., Kim, Y., & Garner, P. (2001). Reduced osmolarity oral rehydration solution for treating dehydration due to diarrhoea in children: Systematic review. \emph{British Medical Journal}, \bold{323}(7304), 81--85. \verb{https://doi.org/10.1136/bmj.323.7304.81} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.hahn2001 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of (log) odds rations using the Mantel-Haenszel method res <- rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, digits=2, slab=study) res ### forest plot (also show studies that were excluded from the analysis) options(na.action="na.pass") forest(res, atransf=exp, xlim=c(-11,9), at=log(c(0.01, 0.1, 1, 10, 100))) options(na.action="na.omit") } } \keyword{datasets} \concept{medicine} \concept{odds ratios} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, odds ratios, Mantel-Haenszel method } metadat/man/dat.pritz1997.Rd0000644000176200001440000001010614750466700015213 0ustar liggesusers\name{dat.pritz1997} \docType{data} \alias{dat.pritz1997} \title{Studies on the Effectiveness of Hyperdynamic Therapy for Treating Cerebral Vasospasm} \description{Results from 14 studies on the effectiveness of hyperdynamic therapy for treating cerebral vasospasm.} \usage{ dat.pritz1997 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{study} \tab \code{numeric} \tab study number \cr \bold{authors} \tab \code{character} \tab study authors \cr \bold{xi} \tab \code{numeric} \tab number of patients that improved with hyperdynamic therapy \cr \bold{ni} \tab \code{numeric} \tab total number of patients treated } } \details{ As described in Zhou et al. (1999), "hyperdynamic therapy refers to induced hypertension and hypervolaemia (volume expansion) to treat ischaemic symptoms due to vasospasm, and the success of this therapy is defined as clinical improvement in terms of neurologic deficits." For each study that was included in the meta-analysis, the dataset includes information on the number of patients that improved under this form of therapy and the total number of patients that were treated. The goal of the meta-analysis is to estimate the true (average) success rate of hyperdynamic therapy. } \source{ Zhou, X.-H., Brizendine, E. J., & Pritz, M. B. (1999). Methods for combining rates from several studies. \emph{Statistics in Medicine}, \bold{18}(5), 557--566. \verb{https://doi.org/10.1002/(SICI)1097-0258(19990315)18:5<557::AID-SIM53>3.0.CO;2-F} } \references{ Pritz M. B., Zhou, X.-H., & Brizendine, E. J. (1996). Hyperdynamic therapy for cerebral vasospasm: A meta-analysis of 14 studies. \emph{Journal of Neurovascular Disease}, \bold{1}, 6--8. Pritz, M. B. (1997). Treatment of cerebral vasospasm due to aneurysmal subarachnoid hemorrhage: Past, present, and future of hyperdynamic therapy. \emph{Neurosurgery Quarterly}, \bold{7}(4), 273--285. } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.pritz1997 dat \dontrun{ ### load metafor package library(metafor) ### computation of "weighted average" in Zhou et al. (1999), Table IV dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0) theta.hat <- sum(dat$ni * dat$yi) / sum(dat$ni) se.theta.hat <- sqrt(sum(dat$ni^2 * dat$vi) / sum(dat$ni)^2) ci.lb <- theta.hat - 1.96 * se.theta.hat ci.ub <- theta.hat + 1.96 * se.theta.hat round(c(estimate = theta.hat, se = se.theta.hat, ci.lb = ci.lb, ci.ub = ci.ub), 4) ### this is identical to an equal-effects model with sample size weights rma(yi, vi, weights=ni, method="EE", data=dat) ### compute sampling variances under the assumption of homogeneity dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0, vtype="AV") dat ### fit equal-effects model (same estimate, but SE is slightly different) rma(yi, vi, data=dat, method="EE") ### under the assumption of homogeneity, the sum of independent binomial ### counts also follows a binomial distribution; this approach yields the same ### estimate and SE as above agg <- escalc(measure="PR", xi=sum(dat$xi), ni=sum(dat$ni)) summary(agg) ### could also compute an 'exact' CI based on the Clopper-Pearson method binom.test(sum(dat$xi), sum(dat$ni)) ### logistic regression model res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat, method="EE") res predict(res, transf=transf.ilogit) ### the results above suggest that the true proportions may be heterogeneous ### random-effects model with raw proportions dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat) res <- rma(yi, vi, data=dat) predict(res) ### random-effects model with logit transformed proportions dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat) res <- rma(yi, vi, data=dat) predict(res, transf=transf.ilogit) ### mixed-effects logistic regression model res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat) predict(res, transf=transf.ilogit) } } \keyword{datasets} \concept{medicine} \concept{single-arm studies} \concept{proportions} \section{Concepts}{ medicine, single-arm studies, proportions } metadat/man/dat.cannon2006.Rd0000644000176200001440000000777614750466700015320 0ustar liggesusers\name{dat.cannon2006} \docType{data} \alias{dat.cannon2006} \title{Studies on the Effectiveness of Intensive Versus Moderate Statin Therapy for Preventing Coronary Death or Myocardial Infarction} \description{Results from 4 trials examining the effectiveness of intensive (high dose) versus moderate (standard dose) statin therapy for preventing coronary death or myocardial infarction.} \usage{ dat.cannon2006 } \format{ The data frame contains the following columns: \tabular{lll}{ \bold{trial} \tab \code{character} \tab trial name \cr \bold{pop} \tab \code{character} \tab study population (post-ACS: post acute coronary syndrome; stable CAD: stable coronary artery disease) \cr \bold{nt} \tab \code{numeric} \tab number of patients in the high dose group \cr \bold{nc} \tab \code{numeric} \tab number of patients in the standard dose group \cr \bold{ep1t} \tab \code{numeric} \tab number of events in the high dose group for end point 1: coronary death or non-fatal myocardial infarction \cr \bold{ep1c} \tab \code{numeric} \tab number of events in the standard dose group for end point 1: coronary death or non-fatal myocardial infarction \cr \bold{ep2t} \tab \code{numeric} \tab number of events in the high dose group for end point 2: coronary death or any cardiovascular event (MI, stroke, hospitalization for unstable angina, or revascularization) \cr \bold{ep2c} \tab \code{numeric} \tab number of events in the standard dose group for end point 2: coronary death or any cardiovascular event (MI, stroke, hospitalization for unstable angina, or revascularization) \cr \bold{ep3t} \tab \code{numeric} \tab number of events in the high dose group for end point 3: cardiovascular death \cr \bold{ep3c} \tab \code{numeric} \tab number of events in the standard dose group for end point 3: cardiovascular death \cr \bold{ep4t} \tab \code{numeric} \tab number of events in the high dose group for end point 4: non-cardiovascular death \cr \bold{ep4c} \tab \code{numeric} \tab number of events in the standard dose group for end point 4: non-cardiovascular death \cr \bold{ep5t} \tab \code{numeric} \tab number of events in the high dose group for end point 5: deaths (all-cause mortality) \cr \bold{ep5c} \tab \code{numeric} \tab number of events in the standard dose group for end point 5: deaths (all-cause mortality) \cr \bold{ep6t} \tab \code{numeric} \tab number of events in the high dose group for end point 6: stroke \cr \bold{ep6c} \tab \code{numeric} \tab number of events in the standard dose group for end point 6: stroke } } \details{ The data were obtained from Figures 2, 3, 4, and 5 in Cannon et al. (2006). The authors used the Mantel-Haenszel method for combining the results from the 4 trials. This approach is implemented in the \code{\link[metafor]{rma.mh}} function. } \source{ Cannon, C. P., Steinberg, B. A., Murphy, S. A., Mega, J. L., & Braunwald, E. (2006). Meta-analysis of cardiovascular outcomes trials comparing intensive versus moderate statin therapy. \emph{Journal of the American College of Cardiology}, \bold{48}(3), 438--445. \verb{https://doi.org/10.1016/j.jacc.2006.04.070} } \author{ Wolfgang Viechtbauer, \email{wvb@metafor-project.org}, \url{https://www.metafor-project.org} } \examples{ ### copy data into 'dat' and examine data dat <- dat.cannon2006 dat \dontrun{ ### load metafor package library(metafor) ### meta-analysis of log odds ratios using the MH method for endpoint 1 res <- rma.mh(measure="OR", ai=ep1t, n1i=nt, ci=ep1c, n2i=nc, data=dat, slab=trial) print(res, digits=2) ### forest plot forest(res, xlim=c(-.8,0.8), atransf=exp, at=log(c(2/3, 1, 3/2)), cex=1.2, xlab="Odds Ratio") mtext("(high dose better)", side=1, line=par("mgp")[1]-0.5, at=log(2/3), cex=1.2, font=3) mtext("(standard dose better)", side=1, line=par("mgp")[1]-0.5, at=log(3/2), cex=1.2, font=3) } } \keyword{datasets} \concept{medicine} \concept{cardiology} \concept{odds ratios} \concept{Mantel-Haenszel method} \section{Concepts}{ medicine, cardiology, odds ratios, Mantel-Haenszel method } metadat/DESCRIPTION0000644000176200001440000000424514750517112013407 0ustar liggesusersPackage: metadat Version: 1.4-0 Date: 2025-02-04 Title: Meta-Analysis Datasets Authors@R: c( person(given = "Wolfgang", family = "Viechtbauer", role = c("aut","cre"), email = "wvb@metafor-project.org", comment = c(ORCID = "0000-0003-3463-4063")), person(given = "Thomas", family="White", role="aut", email = "thomas.white@sydney.edu.au", comment = c(ORCID = "0000-0002-3976-1734")), person(given = "Daniel", family="Noble", role="aut", email = "daniel.noble@anu.edu.au", comment = c(ORCID = "0000-0001-9460-8743")), person(given = "Alistair", family="Senior", role="aut", email = "alistair.senior@sydney.edu.au", comment = c(ORCID = "0000-0001-9805-7280")), person(given = "W. Kyle", family="Hamilton", role="aut", email = "whamilton@ucmerced.edu", comment = c(ORCID = "0000-0002-8642-7990")), person(given = "Guido", family = "Schwarzer", role = "dtc", email = "guido.schwarzer@uniklinik-freiburg.de", comment = c(ORCID = "0000-0001-6214-9087"))) Depends: R (>= 4.0.0) Imports: utils, tools, mathjaxr Suggests: metafor, numDeriv, BiasedUrn, dfoptim, igraph, ape, testthat, digest, lme4, clubSandwich, meta, netmeta, mvtnorm, gridExtra, rms, bayesmeta, ellipse Description: A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. License: GPL (>= 2) ByteCompile: TRUE LazyData: TRUE Encoding: UTF-8 RdMacros: mathjaxr BuildManual: TRUE URL: https://github.com/wviechtb/metadat, https://wviechtb.github.io/metadat/ BugReports: https://github.com/wviechtb/metadat/issues NeedsCompilation: no Packaged: 2025-02-04 21:32:50 UTC; wviechtb Author: Wolfgang Viechtbauer [aut, cre] (), Thomas White [aut] (), Daniel Noble [aut] (), Alistair Senior [aut] (), W. Kyle Hamilton [aut] (), Guido Schwarzer [dtc] () Maintainer: Wolfgang Viechtbauer Repository: CRAN Date/Publication: 2025-02-04 23:10:02 UTC