Bannick Marlena S, Shao Jun, Liu Jingyi, Du Yu, Yi Yanyao, Ye Ting
Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Box 351617, Seattle, Washington 698195, USA.
Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, Wisconsin 653706, USA.
Biometrika. 2025 Apr 12;112(3):asaf029. doi: 10.1093/biomet/asaf029. eCollection 2025.
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.
在随机临床试验中，对基线协变量进行调整可以提高证明和量化治疗效果的可信度和效率。本文研究增强逆倾向加权估计量，它是一种协变量调整的一般形式，使用线性、广义线性以及非参数或机器学习模型来估计给定协变量时响应的条件均值。在协变量自适应随机化下，我们建立了一般性定理，这些定理全面展示了增强逆倾向加权估计量的渐近正态性、效率提升和适用性。特别地，我们首次为在协变量自适应随机化下对相关数据使用交叉拟合的机器学习方法提供了严格的理论依据。基于这些一般性定理，我们深入探讨了在不同随机化方案下保证效率提升和普遍适用性的条件，这也促使我们在应用增强逆倾向加权估计量后使用一些构造的协变量进行联合校准策略。
