Open Access
June 2012 Asymptotic properties of covariate-adaptive randomization
Yanqing Hu, Feifang Hu
Ann. Statist. 40(3): 1794-1815 (June 2012). DOI: 10.1214/12-AOS983


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.


Download Citation

Yanqing Hu. Feifang Hu. "Asymptotic properties of covariate-adaptive randomization." Ann. Statist. 40 (3) 1794 - 1815, June 2012.


Published: June 2012
First available in Project Euclid: 16 October 2012

zbMATH: 1257.62104
MathSciNet: MR3015044
Digital Object Identifier: 10.1214/12-AOS983

Primary: 60F15 , 62G10
Secondary: 60F05 , 60F10

Keywords: Balancing covariates , clinical trial , marginal balance , Markov chain , Pocock and Simon’s design , stratified permuted block

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.40 • No. 3 • June 2012
Back to Top