Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.

ABSTRACT: Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we pro

SUBMITTER: Balzer LB 

PROVIDER: S-EPMC10102904 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature