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

Journal article
Authors Collaboration Fibrinogen Studies
Annika Rosengren
Lars Wilhelmsen
Georg Lappas
Henry Eriksson
Published in Statistics in medicine
Volume 28
Issue 8
Pages 1218-37
ISSN 0277-6715
Publication year 2009
Published at Institute of Medicine, Department of Emergeny and Cardiovascular Medicine
Pages 1218-37
Language en
Links dx.doi.org/10.1002/sim.3540
Keywords Cohort Studies, Computer Simulation, Coronary Disease, metabolism, Data Interpretation, Statistical, Female, Fibrinogen, analysis, Humans, Male, Meta-Analysis as Topic, Models, Statistical
Subject categories Medical and Health Sciences

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

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