Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.

Automated summary

The Bayesian paradigm is ideal for updating uncertainties and making predictions based on data. Bayesian predictive power (BPP) is crucial for assessing the success of a clinical trial. This paper provides mathematical expressions and a design framework to help practitioners utilize BPP in adaptive trial design.