ASYMPTOTIC PROPERTIES OF COVARIATE-ADAPTIVE RANDOMIZATION

• 成果类型: Article
• 署名作者: Hu, Yanqing; Hu, Feifang
• 署名单位: University of Virginia
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS983
• 发表日期: 2012
• 页码: 1794-1815
• 关键词: biased coin designs; sequential clinical-trials; treatment allocation; prognostic-factors; urn models; cancer; stratification; minimization; THEOREMS; therapy

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.