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Int J Epidemiol. 2010 Oct;39(5):1345-59. doi: 10.1093/ije/dyq063. Epub 2010 May 3.

Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies.

Collaborators (245)

Thompson SG, Kaptoge S, White IR, Wood AM, Perry PL, Danesh J, Tipping RW, Ford CE, Simpson LM, Walldius G, Jungner I, Chambless LE, Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman M, Goldbourt U, Benderly M, Tanne D, Whincup PH, Wannamethee SG, Morris RW, Willeit J, Kiechl S, Santer P, Mayr A, Lawlor DA, Yarnell JW, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Tracy RP, Tybjærg-Hansen A, Nordestgaard BG, Benn M, Frikke-Schmidt R, Giampaoli S, Palmieri L, Panico S, Vanuzzo D, Gómez de la Cámara A, Gómez-Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee AJ, Taylor J, Guralnik JM, Wallace R, Guralnik J, Blazer DG, Guralnik JM, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Giampaoli S, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Vasan RS, Pencina MJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Lappas G, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Kiyohara Y, Arima H, Doi Y, Ninomiya T, Rodriguez B, Dekker JM, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Goldbourt U, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Salonen JT, Tuomainen TP, Deeg DJ, Poppelaars JL, Meade TW, Cooper JA, Hedblad B, Berglund G, Engstrom G, Verschuren WM, Blokstra A, Cushman M, Shea S, Döring A, Koenig W, Meisinger C, Mraz W, Bas Bueno-de-Mesquita H, Rosengren A, Lappas G, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum R, Mussolino M, Rimm E, Hankinson S, Manson JE, Pai JK, Meade TW, Cooper JA, Knottenbelt C, Cooper JA, Bauer KA, Sato S, Kitamura A, Naito Y, Iso H, Holme I, Selmer R, Tverdal A, Nystad W, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Crespo CJ, Garcia MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Shepherd J, Packard CJ, Sattar N, Ford I, Cantin B, Després JP, Dagenais GR, Barrett-Connor E, Wingard DL, Bettencourt R, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler M, Hofman A, Tunstall-Pedoe H, Tavendale R, Lowe GD, Woodward M, Ben-Shlomo Y, Davey-Smith G, Howard BV, Zhang Y, Umans J, Onat A, Meade TW, Wilsgaard T, Ingelsson E, Lind L, Giedraitis V, Lannfelt L, Gaziano JM, Ridker P, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Wassertheil-Smoller S, Manson JE, Marmot M, Clarke R, Collins R, Brunner E, Shipley M, Buring J, Shepherd J, Cobbe SM, Ford I, Robertson M, He Y, Marín Ibañez A, Feskens EJ, Kromhout D, Collins R, Di Angelantonio E, Erqou S, Kaptoge S, Lewington S, Orfei L, Pennells L, Perry PL, Ray KK, Sarwar N, Alexander M, Thompson A, Thompson SG, Walker M, Watson S, Wensley F, White IR, Wood AM, Danesh J.

Author information

MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.



Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure-risk relationships, but involves a number of analytical challenges.


This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes.


Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure-risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure-risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available.


Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses.

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