Literature DB >> 35912316

Multi-arm covariate-adaptive randomization.

Feifang Hu1, Xiaoqing Ye2, Li-Xin Zhang3.   

Abstract

Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials. Balancing treatment allocation for influential covariates has become increasingly important in today's clinical trials. The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study. Pocock and Simon's procedure has been extended to the multi-arm case. However, the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades. In this paper, we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case, and establish the corresponding theory under widely satisfied conditions. The theoretical results provide new insights about balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization. Furthermore, these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariate-adaptive randomization procedures. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Entities:  

Keywords:  Hu and Hu’s general procedure; Markov chain; Pocock and Simon’s procedure; balancing covariate; clinical trial; marginal balance; multiple treatment; stratified permuted block design

Year:  2022        PMID: 35912316      PMCID: PMC9326148          DOI: 10.1007/s11425-020-1954-y

Source DB:  PubMed          Journal:  Sci China Math        ISSN: 1869-1862            Impact factor:   1.157


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