In randomized clinical trials, adjusting for baseline covariates has been advocated as a way to 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 includes approaches using linear and generalized linear models and machine learning models. Under covariate-adaptive randomization, we establish a general theorem that shows a complete picture about the asymptotic normality, efficiency gain, and applicability of AIPW estimators. Based on the general theorem, we provide 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. We illustrate the application of the general theorem with two examples, the generalized linear model and the machine learning model. We provide the first theoretical justification of using machine learning methods with dependent data under covariate-adaptive randomization. Our methods are implemented in the R package RobinCar.

翻译：在随机临床试验中，调整基线协变量被视为提高治疗效应展示与量化的可信度及效率的有效途径。本文研究了增强逆概率加权（AIPW）估计量，这是一种包括线性模型、广义线性模型及机器学习模型在内的协变量调整通用形式。在协变量自适应随机化条件下，我们建立了一般的理论框架，全面揭示了AIPW估计量的渐近正态性、效率增益及适用性。基于该理论，我们阐明了不同随机化方案下确保效率增益及普适性的条件，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。通过广义线性模型与机器学习模型两个实例，我们展示了该一般理论的应用。本文首次为协变量自适应随机化下依赖数据使用机器学习方法提供了理论依据。所提方法已在R语言包RobinCar中实现。