Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.

ABSTRACT:

Background

Cohort collaborations often require meta-analysis of exposure-outcome association estimates across cohorts as an alternative to pooling individual-level data that requires a laborious process of data harmonization on individual-level data. However, it is likely that important confounders are not all measured uniformly across the cohorts due to differences in study protocols. This imbalance in measurement of confounders leads to association estimates that are not comparable across cohorts and impedes the meta-analysis of results.

Methods

In this article, we empirically show some asymptotic relations between fully adjusted and unadjusted exposure-outcome effect estimates, and provide theoretical justification for the same. We leverage these results to obtain fully a

SUBMITTER: Ray D 

PROVIDER: S-EPMC9118777 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature