A general form of covariate adjustment in clinical trials under covariate-adaptive randomization
研究了在协变量自适应随机化下，使用线性、广义线性及非参数或机器学习模型进行协变量调整的增强逆概率加权估计量，证明了其渐近正态性、效率增益和适用性，并首次为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。
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.