Bayesian predictive probability based on a bivariate index vector for single-arm phase II study with binary efficacy and safety endpoints

Abstract: In oncology, phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent phase III trials. Traditionally, phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a phase III study at the scheduled end of the trial. Herein, we propose a new simple index vector for summarizing results that cannot be captured by existing strategies. Specifically, for each interim monitoring time point, we calculate the Bayesian predictive probability using our new index and use it to assign a go/no-go decision. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.