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Response-adaptive treatment allocation for non-inferiority trials with heterogeneous variances

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  • Xu, Wenfu
  • Gao, Jingya
  • Hu, Feifang
  • Cheung, Siu Hung

Abstract

In clinical studies, patients usually accrue sequentially. The response-adaptive design has been shown to be a valuable treatment allocation apparatus that skews the treatment allocation probabilities to achieve certain objectives such as reducing the number of patients who receive inferior treatments. The doubly adaptive biased coin design was successfully derived for the three-arm non-inferiority (NI) trial. For an NI study, an experimental treatment can be considered a possible substitute for the standard treatment if the loss of clinically tolerable efficacy is compensated by benefits such as the alleviation of side effects. Previous applications of the doubly adaptive biased coin design in NI trials were developed only for homogeneous treatment variances. However, it is worth to examine the more complicated, but nevertheless popular, scenarios in which the treatment variances are heterogeneous. The proposed treatment allocation scheme is superior when the treatment variances differ and remains very competitive when they are homogeneous. A clinical example is given for demonstrative purposes.

Suggested Citation

  • Xu, Wenfu & Gao, Jingya & Hu, Feifang & Cheung, Siu Hung, 2018. "Response-adaptive treatment allocation for non-inferiority trials with heterogeneous variances," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 168-179.
    • Handle: RePEc:eee:csdana:v:124:y:2018:i:c:p:168-179
    DOI: 10.1016/j.csda.2018.03.005
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    References listed on IDEAS

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    1. Hu, Feifang & Rosenberger, William F., 2003. "Optimality, Variability, Power: Evaluating Response-Adaptive Randomization Procedures for Treatment Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 671-678, January.
    2. Tymofyeyev, Yevgen & Rosenberger, William F. & Hu, Feifang, 2007. "Implementing Optimal Allocation in Sequential Binary Response Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 224-234, March.
    3. Yi Cheng & Donald A. Berry, 2007. "Optimal adaptive randomized designs for clinical trials," Biometrika, Biometrika Trust, vol. 94(3), pages 673-689.
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