Priors from Envisioned Posterior Judgments: A Novel Elicitation Approach With Application to Bayesian Clinical Trials

Abstract

The uptake of formalized prior elicitation from experts in Bayesian clinical trials has been limited, largely due to the challenges associated with complex statistical modeling, the lack of practical tools, and the cognitive burden on experts required to quantify their uncertainty using probabilistic language. Additionally, existing methods do not address prior-posterior coherence, i.e., does the posterior distribution, obtained mathematically from combining the estimated prior with the trial data, reflect the expert's actual posterior beliefs? We propose a new elicitation approach that seeks to ensure prior-posterior coherence and reduce the expert's cognitive burden. This is achieved by eliciting responses about the expert's envisioned posterior judgments under various potential data outcomes and inferring the prior distribution by minimizing the discrepancies between these responses and the expected responses obtained from the posterior distribution. The feasibility and potential value of the new approach are illustrated through an application to a real trial currently underway.