Model-robust and efficient covariate adjustment for cluster-randomized experiments.

ABSTRACT: Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods-generalized estimating equations and linear mixed models-with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose efficient estimators for each estimand that allow for flexible covariate adjustment and additio

SUBMITTER: Wang B 

PROVIDER: S-EPMC11795269 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature