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Response‐adaptive rerandomization

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  • Hengtao Zhang
  • Guosheng Yin

Abstract

Rerandomization has recently attracted more attention in the literature randomized experiments. It leverages covariate information of participants to achieve a well‐balanced allocation, and thus improves the efficiency of inference. However, by only considering covariate information, it may lead to potential ethical issues in clinical trials as a large number of patients might be assigned to the inferior treatment arm. To mitigate this issue, we propose a response‐adaptive rerandomization scheme by incorporating response information for two‐arm comparative clinical trials. Not only is our method applicable to both continuous and binary outcomes, but it also demonstrates desirable statistical and ethical properties. Extensive simulation studies are performed to illustrate the practicality and superiority of our approach.

Suggested Citation

  • Hengtao Zhang & Guosheng Yin, 2021. "Response‐adaptive rerandomization," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1281-1298, November.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1281-1298
    DOI: 10.1111/rssc.12513
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    References listed on IDEAS

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    1. 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.
    2. Peter F. Thall & Lurdes Y. T. Inoue & Thomas G. Martin, 2002. "Adaptive Decision Making in a Lymphocyte Infusion Trial," Biometrics, The International Biometric Society, vol. 58(3), pages 560-568, September.
    3. Xinran Li & Peng Ding, 2020. "Rerandomization and regression adjustment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 241-268, February.
    4. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    5. Wei Ma & Yichen Qin & Yang Li & Feifang Hu, 2020. "Statistical Inference for Covariate-Adaptive Randomization Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1488-1497, July.
    6. 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.
    7. Jianhua Hu & Hongjian Zhu & Feifang Hu, 2015. "A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 357-367, March.
    8. William F. Rosenberger & Nigel Stallard & Anastasia Ivanova & Cherice N. Harper & Michelle L. Ricks, 2001. "Optimal Adaptive Designs for Binary Response Trials," Biometrics, The International Biometric Society, vol. 57(3), pages 909-913, September.
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