A general form of covariate adjustment in clinical trials under covariate-adaptive randomization.

ABSTRACT: In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, efficiency gain and applicability of augmented inverse propensity weighted estimators. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covar

SUBMITTER: Bannick MS 

PROVIDER: S-EPMC12264724 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature