Thomas Shannon D, Kaizer Alexander M
Department of Biostatistics and Informatives, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae086.
This discussion provides commentary on the paper by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim entitled "LEAP: the latent exchangeability prior for borrowing information from historical data". The authors propose a novel method to bridge the incorporation of supplemental information into a study while also identifying potentially exchangeable subgroups to better facilitate information sharing. In this discussion, we highlight the potential relationship with other Bayesian model averaging approaches, such as multisource exchangeability modeling, and provide a brief numeric case study to illustrate how the concepts behind latent exchangeability prior may also improve the performance of existing methods. The results provided by Alt et al. are exciting, and we believe that the method represents a meaningful approach to more efficient information sharing.
本讨论对伊桑·M·阿尔特、常秀亚、江 Xun、刘清、莫梅、H·艾米·夏和约瑟夫·G·易卜拉欣所著的题为《LEAP:从历史数据中借用信息的潜在可交换性先验》的论文进行了评论。作者提出了一种新颖的方法,既能将补充信息纳入研究,又能识别潜在的可交换亚组,以更好地促进信息共享。在本讨论中,我们强调了它与其他贝叶斯模型平均方法(如多源可交换性建模)的潜在关系,并提供了一个简短的数值案例研究,以说明潜在可交换性先验背后的概念如何也能提高现有方法的性能。阿尔特等人提供的结果令人兴奋,我们认为该方法代表了一种实现更高效信息共享的有意义的途径。