While the practice of evidence-based medicine (EBM) requires clinicians to use ‘current best evidence in making decisions about the care of individual patients’[1] it is often unclear what ‘current best evidence’ is and how to use it (if any) for a particular patient of a particular condition. In this editorial, we argue that the emerging approach of Bayesian statistical methods can provide a natural and useful framework for EBM, allowing us to assess and integrate the available evidence and associated uncertainty for use in clinical decision making for individual patients.

中文翻译：

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贝叶斯基于证据的决策方法

尽管循证医学的实践要求临床医生使用“当前最佳证据来决定对患者的护理” [1]，但通常不清楚“当前最佳证据”是什么以及如何使用（如果任何）针对特定疾病的特定患者。在这篇社论中，我们认为贝叶斯统计方法的新兴方法可以为EBM提供自然而有用的框架，从而使我们能够评估和整合可用证据和相关不确定性，以用于个别患者的临床决策。