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

D. Jackson, I. White, J.B. Kostis, A.C. Wilson, A.R. Folsom, E.J.M. Feskens

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)1218-1237
JournalStatistics in Medicine
Volume28
Issue number8
DOIs
Publication statusPublished - 2009

Keywords

  • logistic-regression analysis
  • patient data
  • event outcomes
  • aggregate data
  • model
  • time
  • heterogeneity

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