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Systematically missing confounders in individual participant data meta‐analysis of observational cohort studies

2009; Wiley; Volume: 28; Issue: 8 Linguagem: Inglês

10.1002/sim.3540

1097-0258

Daniel Jakson, Ian R. White, John B. Kostis, A. C. Wilson, Aaron R. Folsom, Kevin Chien‐Chang Wu, Lloyd E. Chambless, U. Benderley, Uri Goldbourt, Johann Willeit, Stefan Kiechl, J. W. G. Yarnell, P M Sweetnam, P. C. Elmwood, M. Cushman, Bruce M. Psaty, Russell P. Tracy, Anne Tybjærg‐Hansen, F. Haverkate, Moniek P.M. de Maat, Simon G. Thompson, F.G.R. Fowkes, Amanda Lee, Fraser Smith, Veikko Salomaa, Kennet Harald, Vesa Rasi, Elina Vahtera, Jari Lahti, R. D'Agostino, W B Kannel, Peter W.F. Wilson, Geoffrey H. Tofler, Daniel Levy, Roberto Marchioli, Franco Valagussa, Annika Rosengren, L. Wilhemsen, Georgios Lappas, H. Eriksson, Peter Cremer, D. Nagel, J. David Curb, Beatriz L. Rodríguez, Ken Yano, J T Salonen, K. Nyyssönen, Tomi‐Pekka Tuomainen, Bo Hedblad, Gunnar Engström, G. Berglund, Hannelore Loewel, Wolfgang Köenig, Hans‐Werner Hense, T W Meade, J. A. Copper, Bianca De Stavola, Clare Knottenbelt, G. J. Miller, Jackie A. Cooper, K. A. Bauer, R D Rosenberg, Shin-ichi Sato, Akihiko Kitamura, Yusuke Naito, Hiroyasu Iso, T. Palosou, Pierre Ducimetière, Philippe Amouyel, Dominique Arveiler, A. E. Evans, Jean Ferrières, I. Juhan‐Vague, A. Bingham, H. Schulte, Gerd Assmann, Bernard Cantin, Benoı̂t Lamarche, Jean‐Philippe Després, Gilles R. Dagenais, Hugh Tunstall‐Pedoe, G.D.O. Lowe, Mark Woodward, Y. Ben-Shlomo, George Davey Smith, Vincenzo Palmieri, J. L. Yeh, Alicja R. Rudnicka, Patrick J. Brennan, P. Ridker, F. Rodeghiero, Alberto Tosetto, J. Shepherd, Donna O. Lowe, I. Ford, Michele Robertson, Eric J. Brunner, M. J. Shipley, E. J.M. Fesken, Emanuele Di Angelantonio,

Statistical Methods in Clinical Trials

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts