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Bayesian Uncertainty Directed Trial Designs

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  • Steffen Ventz
  • Matteo Cellamare
  • Sergio Bacallado
  • Lorenzo Trippa

Abstract

Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Suggested Citation

  • Steffen Ventz & Matteo Cellamare & Sergio Bacallado & Lorenzo Trippa, 2019. "Bayesian Uncertainty Directed Trial Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 962-974, July.
    • Handle: RePEc:taf:jnlasa:v:114:y:2019:i:527:p:962-974
    DOI: 10.1080/01621459.2018.1497497
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    Cited by:

    1. Gary L. Rosner & Peter Müller, 2020. "Discussion on “Predictively consistent prior effective sample sizes,” by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan," Biometrics, The International Biometric Society, vol. 76(2), pages 599-601, June.

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