Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-07-01 DOI:10.1093/biomtc/ujae086
Shannon D Thomas, Alexander M Kaizer
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引用次数: 0

Abstract

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.

关于 Ethan M. Alt、Xiuya Chang、Xun Jiang、Qing Liu、May Mo、H. Amy Xia 和 Joseph G. Ibrahim 所著《LEAP:从历史数据中借用信息的潜在可交换性先验》的讨论。
本讨论对 Ethan M. Alt、Xiuya Chang、Xun Jiang、Qing Liu、May Mo、H. Amy Xia 和 Joseph G. Ibrahim 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。作者提出了一种新方法,在将补充信息纳入研究的同时,还能识别潜在的可交换子群,从而更好地促进信息共享。在讨论中,我们强调了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字案例研究,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。Alt 等人提供的结果令人振奋,我们相信该方法是实现更高效信息共享的一种有意义的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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