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.

翻译：贝叶斯临床试验中专家形式化先验启发的应用一直有限，这主要源于复杂统计建模带来的挑战、实用工具的缺乏，以及专家需要使用概率语言量化其不确定性所带来的认知负担。此外，现有方法未能解决先验-后验一致性问题，即通过将估计的先验与试验数据数学结合得到的后验分布，是否真实反映了专家的实际后验信念？我们提出了一种新的启发方法，旨在确保先验-后验一致性并减轻专家的认知负担。该方法通过启发专家对不同潜在数据结果下其设想后验判断的回应，并通过最小化这些回应与从后验分布获得的预期回应之间的差异来推断先验分布，从而实现上述目标。通过应用于一项当前正在进行的真实试验，我们展示了新方法的可行性和潜在价值。