Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 484 - Applied Bayesian Methodology
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #323560
Title: Bayesian Uncertainty Directed Designs with Delayed Outcomes
Author(s): Daniel Schwartz* and Yuan Ji
Companies: University of Chicago and The University of Chicago
Keywords: Sequential Monte Carlo; Adaptive Randomization; Bayesian model averaging; Uncertainty Quantification; Information Theory; Dose-ranging Trial

The Bayesian uncertainty-directed (BUD) design is an attractive adaptive design for multi-arm trials that randomizes patients to arms relative to how much new information the assignment is expected to generate about the key trial goal, given currently observed data. However, standard BUD designs assume that when new patients are enrolled all previous patients' outcomes have already been observed, and in the common trial setting where this assumption is false they can make poor patient assignments. In this work we extend the BUD information metric to account for previous patients' pending outcomes so a much broader range of multi-arm clinical trials can benefit from the BUD approach. This more general information metric, which integrates over the posterior predictive distribution of the pending outcomes, can be substantially more computationally demanding so we develop an improved Sequential Monte Carlo strategy using a simple identity from information theory to keep these designs practical.

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

Back to the full JSM 2022 program