Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, 100872, China.
Department of Operations, Business Analytics, and Information Systems, University of Cincinnati, Cincinnati, OH 45208, United States.
Biometrics. 2024 Mar 27;80(2). doi: 10.1093/biomtc/ujae047.
In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.
在比较研究中，协变量平衡和序贯分配方案引起了越来越多的学术关注。虽然许多从理论上证明合理的适应性随机化方法能够实现协变量平衡，但它们通常是将患者分配成对或组。为了更好地满足临床医生出于某些经济或伦理原因无法等待其他参与者为当前患者分配的实际需求，我们提出了一种单独和序贯为患者随机分配的方法。该方法从概念上将由新提出的修正马氏距离衡量的协变量不平衡与边际不平衡（即两组之间的样本量差异）分开，并以明确的优先级最小化它们。与现有的序贯随机化方法相比，该方法在直接保持边际平衡的同时，实现了最佳的协变量平衡，为我们提供了对随机化过程的更多控制。我们通过广泛的模拟研究和真实数据分析证明了该方法的优越性能，并从不平衡度量的收敛性和随后的治疗效果估计两个方面为该方法提供了理论保证。
