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

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.