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

• 成果类型: Article
• 署名作者: Bannick, Marlena S.; Shao, Jun; Liu, Jingyi; Du, Yu; Yi, Yanyao; Ye, Ting
• 署名单位: University of Washington; University of Washington Seattle; University of Wisconsin System; University of Wisconsin Madison; Eli Lilly
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaf029
• 发表日期: 2025
• 关键词: binary outcomes; EFFICIENCY

摘要

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.