Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.

ABSTRACT: Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneou

SUBMITTER: Blette BS 

PROVIDER: S-EPMC11041086 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature