Abstract
Prediction of satisfaction design, with binary endpoints, is an innovative strategy for phase II trials. We explain this hybrid frequentist-Bayesian strategy with an adept statistical plan and thorough findings, incorporating a description of study design features such as the sample size and the beta prior distribution, to simplify the Bayesian design. We also provide a set of tables and figures ranging from the stopping boundary for futility to the prediction of satisfaction, performance (type I error, power, and the probability of early termination PET), and sensitivity analysis for prediction of satisfaction. The statistical plan includes the operating characteristics through simulation study. Several trial examples from phase II lung cancer studies demonstrate the approach's practical use. The prediction of satisfaction design presents a flexible method in clinical study. This statistical study adds value to the application by broadening its scope.