Probability of Success for Establishing Noninferiority Across Multiple Visits: Extension of Covariate‐Adjusted Bayesian Hierarchical Modeling Framework

Effective decision-making plays a vital role throughout the drug development process, particularly when a proof-of-concept (POC) or phase II study has been completed. To determine whether to proceed to a larger-scale, confirmatory phase III study, assessing the uncertainty about the underlying treatment effect and the probability of success (POS) in the phase III study is of critical importance. In this paper, we proposed and investigated a Bayesian covariate-adjusted hierarchical modeling approach leveraging historical data with longitudinal outcome to quantitatively assess the POS of the confirmatory phase III trial. Although historical data borrowing methods are widely used and known for the advantages in alleviating recruitment and ethical challenges as well as improving trial operational efficiency, its application to predicting future trial POS with longitudinal outcome over multiple visits pose methodological challenges. This paper not only provided a comprehensive modeling approach but also demonstrated how the proposed model can be used in a Go/No-Go decision-making framework with a glaucoma eye care project example. For the approval of new drugs targeting glaucoma, regulatory agencies typically require a pivotal phase III trial to demonstrate noninferiority compared to a standard of care treatment. This may involve meeting both statistical and clinical margins across multiple visits simultaneously. Simulations were performed to evaluate the key factors that affect the operating characteristics, such as between-trial heterogeneity, subject-level variance and between-visit correlation. The proposed decision-making framework can also be applied to studies in other therapeutical areas with similar settings.