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

Daniel Jakson, Ian White, J. B. Kostis, A. C. Wilson, A. R. Folsom, K. Wu, L. Chambless, U. Benderley, U. Goldbourt, J. Willeit, S. Kiechl, J. W.G. Yarnell, P. M. Sweetnam, P. C. Elmwood, M. Cushman, B. M. Psaty, R. P. Tracy, A. Tybjærg-Hansen, F. Haverkate, M. P.M. de MaatS. G. Thompson, F. G.R. Fowkes, A. J. Lee, F. B. Smith, V. Salomaa, K. Harald, V. Rasi, E. Vahtera, P. Jousilahti, R. D'Agostino, W. B. Kannel, P. W.F. Wilson, G. Tofler, D. Levy, R. Marchioli, F. Valagussa, A. Rosengren, L. Wilhemsen, G. Lappas, H. Eriksson, P. Cremer, D. Nagel, J. D. Curb, B. Rodriguez, K. Yano, J. T. Salonen, K. Nyyssönen, T. P. Tuomainen, B. Hedblad, G. Engström, G. Berglund, H. Loewel, W. Koenig, H. W. Hense, T. W. Meade, J. A. Copper, B. De Stavola, C. Knottenbelt, G. J. Miller, J. A. Cooper, K. A. Bauer, R. D. Rosenberg, S. Sato, A. Kitamura, Y. Naito, H. Iso, T. Palosou, P. Ducimetiere, P. Amouyel, D. Arveiler, A. E. Evans, J. Ferrieres, I. Juhan-Vague, A. Bingham, H. Schulte, G. Assmann, B. Cantin, B. Lamarche, J. P. Despres, G. R. Dagenais, H. Tunstall-Pedoe, G. D.O. Lowe, M. Woodward, Y. Ben-Shlomo, G. Davey Smith, V. Palmieri, J. L. Yeh, A. Rudnicka, P. Brennan, P. Ridker, F. Rodeghiero, A. Tosetto, J. Shepherd, D. O. Lowe, I. Ford, M. Robertson, E. Brunner, M. Shipley, E. J.M. Fesken, E. Di Angelantonio

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1218-1237
Number of pages20
JournalStatistics in Medicine
Volume28
Issue number8
DOIs
StatePublished - Apr 15 2009
Externally publishedYes

Keywords

  • Confounders
  • Meta-analysis
  • Missing covariates
  • Observational studies
  • Survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint

Dive into the research topics of 'Systematically missing confounders in individual participant data meta-analysis of observational cohort studies'. Together they form a unique fingerprint.

Cite this