Assessing predictive probability of success for future clinical trials.

Abstract

Data-driven decision-making is crucial in drug development, with the predictive probability of success (PoS) being a key quantitative tool. PoS estimates the likelihood of success of a future trial based on the same or surrogate endpoint(s) of interest, utilizing information from interim analyses, or completed historical studies. While it has been extensively studied and broadly applied in clinical practice, there is a growing need of a unified approach for PoS that can effectively incorporate information from surrogate endpoints and multiple historical studies. This paper investigates and assesses a unified Bayesian approach for PoS. We first review PoS based on historical data on the same endpoint and then extend it to include information from a surrogate endpoint with a closed-form solution. Additionally, we utilize a Bayesian meta-analytic approach to incorporate data from multiple historical studies. We illustrate the unified approach with examples from oncology and immunology trials and provide an R package "PPoS" for practical implementation. By integrating the assessment of PoS with information from surrogate endpoints and historical studies, we aim to enhance the decision-making process in drug development.