从先验到性能：利用贝叶斯动态借用提升统计效率
From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing
AI总结：
本文全面回顾贝叶斯动态借用方法，并展示如何利用公开软件实现模型选择、效应估计和敏感性分析，以增强分析人员整合外部数据的信心。
AI中文摘要：
贝叶斯统计推断方法的主要优势之一在于其灵活性，能够整合来自各种来源的信息，从专家意见到历史数据。尽管关于贝叶斯动态借用的文献丰富，但关于如何实施此类方法的实用指导相对匮乏。我们全面回顾了最常见的外部数据动态借用方法，包括如何整合多个历史数据集。然后，我们展示了如何利用公开可用的软件在实践中应用这些技术，以执行重要的统计任务，如模型选择、平均处理效应估计和先验敏感性分析。目的是让分析人员在使用贝叶斯方法整合外部数据时更加自信。所有代码均在配套的GitHub仓库中公开提供（此HTTPS URL）。
英文摘要：
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).
