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Activity Number: 494 - Clinical Trial Design for Precision Oncology
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #326945
Title: Bayesian Uncertainty Directed Trial Designs
Author(s): Lorenzo Trippa*
Companies: Harvard
Keywords: Multi-arm clinical trials; Information theory; Decision theory; Response- adaptive designs

Most Bayesian response-adaptive designs unbalance randomization rates towards 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 trials. 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.

Authors who are presenting talks have a * after their name.

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