Data-Driven Prior Distributions for A Bayesian Phase-2 COPD Dose-Finding Clinical Trial

Abstract

<p>The prior distribution reflects knowledge and uncertainty of the modeled parameters. Determining the prior distribution for a dose-finding clinical trial can be influential in its design and analysis. Using the planning of a phase 2 trial for COPD with a dose-response curve as a case study, we illustrate the use of relevant historical data for the nonlinear curve mean-model parameters as well as consideration for terms to characterize between-trial and within-trial variability. Through a predictive inference exercise, a data-driven informative prior distribution is constructed for the future study. We share our strategies on how to obtain informative Bayesian priors for both design and analysis of dose-finding clinical trials using relevant historical data and deal with the associated issues.</p