From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing

2026-09-12

AI Summary

本文通过回顾和实践指导贝叶斯动态借用方法，利用公开软件解决模型选择、平均治疗效果估计等问题，增强分析师使用贝叶斯方法的信心。

Abstract

One of the main advantages of the Bayesian approach to statistical inference is the flexibility in incorporating information from various sources, from expert opinion to historical data. Whereas the literature on Bayesian dynamic borrowing is rich, practical guidance for how to implement such methods is comparatively sparse. We thoroughly review the most common dynamic borrowing approaches for external data, including how to incorporate multiple historical data sets. We then show how these techniques can be used in practice, using publicly available software, to perform important statistical tasks such as model selection, average treatment effect estimation and prior sensitivity analysis. The aim is to make analysts more confident in their use of Bayesian methods to incorporate external data. All code is made publicly available in a companion GitHub repository (https://github.com/EzequielEBS/hdbayes-tutorial).

Problem

• Bayesian dynamic borrowing
• external data
• statistical inference

Innovation

• Bayesian dynamic borrowing
• external data
• model selection
• average treatment effect estimation
• prior sensitivity analysis