Heuts Samuel, Kawczynski Michal J, Velders Bart J J, Brophy James M, Hickey Graeme L, Kowalewski Mariusz
Department of Cardiothoracic Surgery, Maastricht University Medical Centre (MUMC+), Maastricht, Netherlands.
Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands.
Eur J Cardiothorac Surg. 2025 Mar 28;67(4). doi: 10.1093/ejcts/ezaf139.
Trials in cardiac surgery are often hampered at the design level by small sample sizes and ethical considerations. The conventional analytical approach, combining frequentist statistics with null hypothesis significance testing, has known limitations and its associated P-values are often misinterpreted, leading to dichotomous conclusions of trial results. The Bayesian statistical framework may overcome these limitations through probabilistic reasoning and is subsequently introduced in this Primer. The Bayesian framework combines prior beliefs and currently obtained data (the likelihood), resulting in updated beliefs, also known as posterior distributions. These distributions subsequently facilitate probabilistic interpretations. Several previous cardiac surgery trials have been performed under a Bayesian framework and this Primer enhances the understanding of their basic concepts by linking results to graphical presentations. Furthermore, contemporary trials that were initially analysed under a frequentist framework, are re-analysed within a Bayesian framework to demonstrate several interpretative advantages.
心脏外科手术试验在设计阶段常常受到样本量小和伦理考量的阻碍。传统的分析方法，即将频率主义统计学与零假设显著性检验相结合，存在已知的局限性，其相关的P值常常被误解，导致对试验结果得出二分法结论。贝叶斯统计框架或许可以通过概率推理克服这些局限性，因此本入门指南引入该框架。贝叶斯框架将先验信念与当前获得的数据（似然性）相结合，产生更新后的信念，也称为后验分布。这些分布随后便于进行概率解释。此前已有多项心脏外科手术试验在贝叶斯框架下开展，本入门指南通过将结果与图形展示相联系，增强了对其基本概念的理解。此外，对最初在频率主义框架下分析的当代试验，在贝叶斯框架内重新进行分析，以展示若干解释优势。
