Brophy James M
McGill University Health Centre, Montréal, Québec, Canada.
Can J Cardiol. 2021 Sep;37(9):1415-1427. doi: 10.1016/j.cjca.2021.03.014. Epub 2021 Mar 26.
The limitations of traditional statistical analyses of randomised clinical trials that follow the frequentist inference paradigm have been increasingly noted. This article discusses the Bayesian approach to statistical inference in randomised clinical trials, demonstrating its functioning, utility, and limitations through an examination of current cardiovascular examples. A simplified overview of the mechanics of Bayesian inference and a glossary of the Bayesian terminology is first provided. The duality of the Bayesian approach, providing both an evidential calculus based on the likelihood ratio and a belief calculus that incorporates our prior beliefs with the current data, is presented. Specific cardiovascular trials are reanalysed with Bayesian methods. It is claimed that the Bayesian approach, by providing an enhanced ability to appreciate and model uncertainty, leads to an enriched understanding of the strength and quantification of the evidence, of the distinction between statistical and clinical significance, of the within- and between-trial variability, of subgroup analyses, of the utility of informative priors, and of our ability to synthesise and update our knowledge base. Ultimately, it is argued that the Bayesian approach is more intuitive and transparent, permits enhanced data analysis and interpretation, and may lead to improved decision making not only by trialists but also by practicing clinicians, guideline writers, and even expert regulatory advisory consultants.
遵循频率主义推断范式的随机临床试验传统统计分析的局限性已日益受到关注。本文讨论了随机临床试验中统计推断的贝叶斯方法，通过审视当前心血管领域的实例展示其运作方式、效用及局限性。首先提供了贝叶斯推断机制的简化概述以及贝叶斯术语表。阐述了贝叶斯方法的双重性，它既提供基于似然比的证据演算，又提供将我们的先验信念与当前数据相结合的信念演算。运用贝叶斯方法对特定心血管试验进行重新分析。据称，贝叶斯方法通过增强理解和模拟不确定性的能力，能使我们更深入地理解证据的强度和量化、统计显著性与临床显著性之间的区别、试验内和试验间的变异性、亚组分析、信息性先验的效用以及我们综合和更新知识库的能力。最终，有人认为贝叶斯方法更直观、更透明，能促进数据分析和解释，不仅可能使试验者，还可能使临床执业医师、指南编写者甚至专家监管咨询顾问做出更好的决策。
