A Novel Framework for Bayesian Response-Adaptive Randomization

  • Jian ZhuEmail author
  • Ina Jazić
  • Yi Liu
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)


The development of response-adaptive randomization (RAR) has taken many different paths over the past few decades. Some RAR schemes optimize certain criteria, but may be complicated and often rely on asymptotic arguments, which may not be suitable in trials with small sample sizes. Some Bayesian RAR schemes are very intuitive and easy to implement, but may not always be tailored toward the study goals. To bridge the gap between these methods, we proposed a framework in which easy-to-implement Bayesian RAR schemes can be derived to target the study goals. We showed that the popular Bayesian RAR scheme that assigns more patients to better performing arms fits in the new framework given a specific intention. We also illustrated the new framework in the setting where multiple treatment arms are compared to a concurrent control arm. Through simulation, we demonstrated that the RAR schemes developed under the new framework outperform a popular method in achieving the pre-specified study goals.


Response-adaptive randomization Bayesian adaptive design Goal function Multi-arm comparative trials 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Global StatisticsTakeda Pharmaceutical Company LimitedTokyoJapan
  2. 2.Department of Biostatistics, T.H. Chan School of Public HealthHarvard UniversityCambridgeUSA
  3. 3.Takeda PharmaceuticalCambridgeUSA

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