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 into the 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.
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Acknowledgements
This work was supported by the National Key R&D Program of China (Grant No. 2018YFC2000302), National Natural Science Foundation of China (Grant Nos. 11731012, 11731011 and 12031005), Ten Thousands Talents Plan of Zhejiang Province (Grant No. 2018R52042) and the Fundamental Research Funds for the Central Universities.
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Hu, F., Ye, X. & Zhang, LX. Multi-arm covariate-adaptive randomization. Sci. China Math. 66, 163–190 (2023). https://doi.org/10.1007/s11425-020-1954-y
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DOI: https://doi.org/10.1007/s11425-020-1954-y
Keywords
- multiple treatment
- balancing covariate
- clinical trial
- marginal balance
- Markov chain
- Hu and Hu’s general procedure
- Pocock and Simon’s procedure
- stratified permuted block design