Introduction to Bayesian Methods for Clinical Trial Design and Sample Size Determination — Professional Development Continuing Education Course
This course is designed to give statisticians with experience in clinical trials research a comprehensive overview of the use of Bayesian methods for trial design and on implementation using standard software. Applications will be demonstrated using R, SAS or both. Part I will give an overview of Bayesian sample size determination with a focus on fixed sample size trials in the phase II/III setting. Focus is paid to four concepts: (1) sampling priors that reflects knowledge about parameter(s) in the data model, (2) fitting priors used to analyze data, (3) Bayesian sample size determination (SSD) criterion, and (4) monitoring strategies. For (3), a review of Bayesian criterion for SSD will be given (e.g., Bayesian power, average coverage criterion). For (4), multiple strategies will be discussed for monitoring (e.g., predictive probability of success, sequential methods). Part II will focus on advanced Bayesian designs that incorporate information borrowing. The types of designs considered fall into two broad categories: (1) designs that borrow information via an informative fitting prior specified a priori based on one or more historical datasets (e.g., pediatric trials that extrapolate from adult trials), and (2) designs that seek to borrow information across subgroups within a trial (e.g., basket trials).
Instructor(s): Matthew A. Psioda, University of North Carolina at Chapel Hill; Joseph G Ibrahim, University of North Carolina