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Keywords: Adaptive randomization, dose-ranging trial, Bayesian model averaging
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, allowing data about one dose’s effectiveness to partially inform the evidence about nearby doses. 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.