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 (AIPW) estimator, which is a general form of covariate adjustment that uses linear, generalized linear, and non-parametric 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 AIPW 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 AIPW. Our methods are implemented in the R package RobinCar.

翻译：在随机临床试验中，调整基线协变量可提高评估治疗效应的可信度与效率。本文研究增强逆概率加权（AIPW）估计量，这是一种利用线性模型、广义线性模型以及非参数或机器学习模型来刻画给定协变量条件下响应变量条件均值的协变量调整一般形式。在协变量自适应随机化框架下，我们建立了一般性定理，系统揭示了AIPW估计量的渐近正态性、效率增益及适用性。特别地，我们首次为协变量自适应随机化下依赖数据的交叉拟合机器学习方法提供了严格的理论依据。基于这些一般定理，我们揭示了不同随机化方案下保证效率增益与普适适用性的条件，并由此提出了一种在应用AIPW后利用构造协变量进行联合校准的策略。本研究方法已在R包RobinCar中实现。