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

Marlena S Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi and Ting Ye

Biometrika, 2025, vol. 112, issue 3, 881-908

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 covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability under different randomization schemes, which also motivate a joint calibration strategy using some constructed covariates after applying augmented inverse propensity weighted estimators.

Keywords: Augmentation; Covariate-adaptive randomization; G-computation; Model-assisted inference; Multiple treatment arms; Nonlinear adjustment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:112:y:2025:i:3:p:881-908.

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