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Balancing covariates in multi-arm trials via adaptive randomization

Author

Listed:
  • Yang, Haoyu
  • Qin, Yichen
  • Wang, Fan
  • Li, Yang
  • Hu, Feifang

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.

Suggested Citation

  • Yang, Haoyu & Qin, Yichen & Wang, Fan & Li, Yang & Hu, Feifang, 2023. "Balancing covariates in multi-arm trials via adaptive randomization," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002225
    DOI: 10.1016/j.csda.2022.107642
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    References listed on IDEAS

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    1. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    2. Kari Lock Morgan & Donald B. Rubin, 2015. "Rerandomization to Balance Tiers of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1412-1421, December.
    3. Zhenzhen Xu & John D. Kalbfleisch, 2013. "Repeated Randomization and Matching in Multi-Arm Trials," Biometrics, The International Biometric Society, vol. 69(4), pages 949-959, December.
    4. Quan Zhou & Philip A Ernst & Kari Lock Morgan & Donald B Rubin & Anru Zhang, 2018. "Sequential rerandomization," Biometrika, Biometrika Trust, vol. 105(3), pages 745-752.
    5. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    6. Abhijit V. Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2020. "A Theory of Experimenters: Robustness, Randomization, and Balance," American Economic Review, American Economic Association, vol. 110(4), pages 1206-1230, April.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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