标题： covariate- adaptive 随机化的渐近性质 显示英文标题

标题： Asymptotic properties of covariate-adaptive randomization

摘要： 在临床试验中，平衡影响较大的协变量的分配至关重要。随着越来越多的生物标志物被发现与转化研究（基因组学、蛋白质组学和代谢组学）中的不同疾病相关联，这一点变得越来越重要。分层随机区组化和最小化方法[如Pocock和Simon在《生物统计学》1975年第31卷第103-115页等]是实践中最常用的两种方法。然而，当分层数量较大时，分层随机区组化无法实现良好的总体平衡，而传统的最小化方法也存在潜在的分层内不平衡问题。此外，最小化方法的理论基础仍然难以捉摸。本文提出了一种新的协变量自适应设计，能够控制各种类型的不平衡。我们证明，在某些条件下，分层内不平衡的联合过程是一个正递归马尔可夫链。因此，这种新程序产生了更平衡的分配。通过广泛的模拟研究，我们也展示了所提出的程序的优势。我们的工作为该领域的未来研究提供了理论工具。 显示英文摘要

摘要： Balancing treatment allocation for influential covariates is critical in clinical trials. This has become increasingly important as more and more biomarkers are found to be associated with different diseases in translational research (genomics, proteomics and metabolomics). Stratified permuted block randomization and minimization methods [Pocock and Simon Biometrics 31 (1975) 103-115, etc.] are the two most popular approaches in practice. However, stratified permuted block randomization fails to achieve good overall balance when the number of strata is large, whereas traditional minimization methods also suffer from the potential drawback of large within-stratum imbalances. Moreover, the theoretical bases of minimization methods remain largely elusive. In this paper, we propose a new covariate-adaptive design that is able to control various types of imbalances. We show that the joint process of within-stratum imbalances is a positive recurrent Markov chain under certain conditions. Therefore, this new procedure yields more balanced allocation. The advantages of the proposed procedure are also demonstrated by extensive simulation studies. Our work provides a theoretical tool for future research in this area.