Bayesian Uncertainty Directed Trial Designs

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.

KEYWORDS:

• Decision theory
• Information theory
• Multi-arm clinical trials
• Response-adaptive designs

Supplementary Materials

The supplementary material contains additional simulations, computational details, proofs of the results in Section 2 and R code to reproduce the examples in sections 3 to 5.

Additional information

Funding

The work of SB was partially supported by funding from the Cantab Capital Institute for the Mathematics of Information. LT has been supported by a Burroughs Wellcome Fund Award for Innovation in Regulatory Science and the The Claudia Adams Barr Program in Innovative Basic Cancer Research.