Use of Composite Priors for Assurance of Sequential Therapy Trials: Practical Experiences Using Binary Endpoint Data

Abstract

Accurately calculating assurance can play an important role in quantifying the confidence in the success of a clinical trial. Formulation of prior distributions and the use of assurance can therefore play a vital part in drug development planning and decision‐making. Although approaches in developing priors and calculating probability of success (assurance) have been readily reported, the application for studies incorporating sequential treatment therapy—where multiple medications or therapeutic regimens are administered consecutively to maximise therapeutic efficacy and minimise adverse effects—is less well defined. Challenges relating to eliciting a prior for a single treatment effect across a sequential therapy include reliance on individual experts' subjective beliefs about translating multiple mechanisms of actions into a single overall treatment effect and the inability to capture nuances across and inconsistency in combining individual treatment component effects. In this paper, we outline a comprehensive quantitative framework for developing a composite prior distribution (from individual component priors) and calculating the assurance for study designs involving sequential treatment. We demonstrate how this framework, which employs both prior elicitation and data‐driven prior development methods, was applied in two phase 2 study designs with sequential treatment regimens using a binary endpoint. We also discuss the optimal conditions for these methods and compare the benefits and challenges with a conventional holistic prior elicitation and assurance framework.