Systematically missing data in causally interpretable meta-analysis.

ABSTRACT: Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estim

SUBMITTER: Steingrimsson JA 

PROVIDER: S-EPMC11017122 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature