Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Department of Critical Care Medicine, Mount Sinai Hospital, Toronto, ON, Canada.
Center for Acute Respiratory Failure, Columbia University College of Physicians and Surgeons and New York-Presbyterian Hospital, New York, NY, USA; Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University College of Physicians and Surgeons and New York-Presbyterian Hospital, New York, NY, USA.
Lancet Respir Med. 2021 Feb;9(2):207-216. doi: 10.1016/S2213-2600(20)30471-9. Epub 2020 Nov 20.
Recent Bayesian reanalyses of prominent trials in critical illness have generated controversy by contradicting the initial conclusions based on conventional frequentist analyses. Many clinicians might be sceptical that Bayesian analysis, a philosophical and statistical approach that combines prior beliefs with data to generate probabilities, provides more useful information about clinical trials than the frequentist approach. In this Personal View, we introduce clinicians to the rationale, process, and interpretation of Bayesian analysis through a systematic review and reanalysis of interventional trials in critical illness. In the majority of cases, Bayesian and frequentist analyses agreed. In the remainder, Bayesian analysis identified interventions where benefit was probable despite the absence of statistical significance, where interpretation depended substantially on choice of prior distribution, and where benefit was improbable despite statistical significance. Bayesian analysis in critical care medicine can help to distinguish harm from uncertainty and establish the probability of clinically important benefit for clinicians, policy makers, and patients.
最近，对危重病领域重要试验的贝叶斯重新分析引起了争议，因为它们与基于传统频率分析的初始结论相矛盾。许多临床医生可能会怀疑贝叶斯分析（一种将先验信念与数据相结合以生成概率的哲学和统计方法）是否比频率分析提供了更有用的临床试验信息。在这篇个人观点中，我们通过对危重病干预试验的系统回顾和重新分析，向临床医生介绍了贝叶斯分析的原理、过程和解释。在大多数情况下，贝叶斯分析和频率分析结果一致。在其余情况下，贝叶斯分析确定了在没有统计学意义的情况下获益可能的干预措施，其中解释在很大程度上取决于先验分布的选择，以及在存在统计学意义的情况下获益不可能的干预措施。贝叶斯分析在重症医学中可以帮助临床医生、决策者和患者区分危害和不确定性，并确定具有临床重要意义的获益的概率。
