Faster and More Informative Phase 2b Dose-Ranging Trials Through Bayesian Uncertainty-Directed Designs with Model Averaging

• Activity Number: 318 - Adaptive (and Other) Clinical Trial Designs
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Daniel Schwartz* and Yuan Ji
• Companies: University of Chicago and The University of Chicago
• Keywords: Adaptive randomization; Dose-ranging trial; Bayesian model averaging; Uncertainty quantification; Sequential Monte Carlo

Abstract

In this paper we make three contributions to the design and analysis of Phase 2b non-oncology dose-ranging trials, which are critical for drug developers to find the optimal dose to carry forward to Phase 3. First, we use a Bayesian "uncertainty-directed" design (Ventz et al. 2018) that adaptively randomizes patients to doses in a way that explicitly maximizes information about which dose is optimal. This typically means assigning new patients to doses that have been previously understudied relative to how strongly the data suggest they could be the optimal dose. Second, we efficiently and robustly incorporate pharmacological knowledge through Bayesian model averaging of parametric dose-response curves. And third, we provide very fast posterior computation for this Bayesian adaptive design using a Sequential Monte Carlo algorithm that makes it easier for trialists to conduct extensive simulation studies to reliably check Frequentist error. These practical designs show promise to accelerate Phase 2b trials and produce higher quality evidence before Phase 3.