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

In oncology, phase II studies are crucial for clinical development plans, as they identify potent agents with sufficient activity to continue development in the subsequent phase III trials. Traditionally, phase II studies are single-arm studies, with an endpoint of treatment efficacy in the short-term. However, drug safety is also an important consideration. Thus, in the context of such multiple outcome design, predictive probabilities-based Bayesian monitoring strategies have been developed to assess if a clinical trial will show a conclusive result at the planned end of the study. In this paper, we propose a new simple index vector for summarizing the 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. This analysis demonstrates that the proposed method makes appropriate interim go/no-go decisions.