Balancing covariates in multi-arm trials via adaptive randomization

Haoyu Yang, Yichen Qin, Fan Wang, Yang Li and Feifang Hu

Computational Statistics & Data Analysis, 2023, vol. 179, issue C

Abstract: Multi-arm trials are common in medical and health research for comparing the efficacy of competing drugs and interventions, among other applications. While ensuring covariate balance is a critical issue for comparative studies to be successful, classical multi-arm trials often fail to balance covariates among multi-treatments. An adaptive randomization via Mahalanobis distance for multi-arm trials is proposed to improve the covariate balance and thus the quality of the subsequent treatment effect estimation. The investigation scope includes the implementation of the proposed method and also its theoretical properties. Both theoretical and numerical results demonstrate the proposed method can attain desirable covariate balance, and thus improving the subsequent estimation efficiency. Compared with other competing methods, the computational cost of the proposed method is also favorable. An illustrative real case analysis of the efficacy of different doses of Canagliflozin, a treatment for patients with type 2 diabetes, also proves that the proposed method has broad applicability.

Keywords: Covariate-adaptive randomization; Mahalanobis distance; Multi-arm trials; Treatment effect (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002225

DOI: 10.1016/j.csda.2022.107642

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