Abstract
In classical group sequential designs, a clinical trial is considered as a success if the experimental treatment is statistically significantly better than placebo. The criteria for stopping or continuing the trial are chosen to control the false-positive rate (type I error). Bayesian group sequential design has an advantage of allowing inclusion of prior information in the analysis. The decision criteria can be based on the posterior or predictive distribution of the treatment effect to stop for success or futility, or to continue for each interim analysis and the final analysis. This chapter introduces Bayesian group sequential designs with examples in a confirmatory setting, including how to calibrate the tuning parameters to set up decision criteria for the interim and final analyses, how to derive the sample size, and how to evaluate the operating characteristics via simulations.
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