Sebastien Bernard
Sanofi R&D Data and Data Science, Clinical Modeling & Evidence Integration, 450 Water Street, Cambridge, MA, 02142, USA.
J Pharmacokinet Pharmacodyn. 2023 Dec;50(6):495-499. doi: 10.1007/s10928-023-09860-0. Epub 2023 May 6.
Application of Bayesian methods is one the tools that can be used to face the multiple challenges that are met when clinical trials must be conducted in rare diseases. We propose in this work to use a dynamic Bayesian borrowing approach, based on a mixture prior, to complement the control arm of a comparative trial and estimate the mixture parameter by an Empirical Bayes approach. The method is compared, using simulations, with an approach based on a pre-specified (non-adaptive) informative prior. The simulation study shows that the proposed method exhibits similar power as the non-adaptive prior and drastically reduce type I error in case of severe discrepancy between the informative prior and the study control arm data. In case of limited discrepancy between the informative prior and the study control arm data, then our proposed adaptive prior does not reduce the inflation of the type I error.
贝叶斯方法的应用是应对罕见病临床试验中遇到的多重挑战时可使用的工具之一。在这项工作中，我们提议使用一种基于混合先验的动态贝叶斯借用方法，以补充比较试验的对照组，并通过经验贝叶斯方法估计混合参数。通过模拟，将该方法与基于预先指定（非自适应）信息先验的方法进行比较。模拟研究表明，所提出的方法与非自适应先验具有相似的检验效能，并且在信息先验与研究对照组数据之间存在严重差异的情况下，能大幅降低I型错误。在信息先验与研究对照组数据之间差异有限的情况下，我们提出的自适应先验不会减少I型错误的膨胀。
