Meta-analysis on studies with heterogeneous and partially observed covariates.

ABSTRACT: Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-

SUBMITTER: Akkaya Hocagil T 

PROVIDER: S-EPMC11200172 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature