Balancing influential covariates is crucial for valid treatment comparisons in clinical studies. While covariate-adaptive randomization is commonly used to achieve balance, its performance can be inadequate when the number of baseline covariates is large. It is therefore essential to identify the influential factors associated with the outcome and ensure balance among these critical covariates. In this article, we propose a novel covariate-adjusted response-adaptive randomization that integrates the patients' responses and covariates information to select sequentially significant covariates and maintain their balance. We establish theoretically the consistency of our covariate selection method and demonstrate that the improved covariate balancing, as evidenced by a faster convergence rate of the imbalance measure, leads to higher efficiency in estimating treatment effects. Furthermore, we provide extensive numerical and empirical studies to illustrate the benefits of our proposed method across various settings.

翻译：平衡有影响力的协变量对于临床研究中有效的治疗比较至关重要。尽管协变量自适应随机化通常用于实现平衡，但当基线协变量数量较大时，其性能可能不足。因此，识别与结局相关的有影响力因素并确保这些关键协变量间的平衡至关重要。本文提出一种新型的协变量调整响应自适应随机化方法，该方法整合患者的响应和协变量信息，依次选择显著性协变量并维持其平衡。我们从理论上证明了协变量选择方法的一致性，并表明通过不平衡度量的更快收敛速度所体现的改进的协变量平衡性，能够提高治疗效应估计的效率。此外，我们提供了广泛的数值和实证研究，以说明所提方法在不同场景下的优势。