Department of Diagnostic Imaging (J.M.O., S.B.C., M.D.H., M.G.), Foothills Medical Center, University of Calgary, AB, Canada.
Department of Clinical Neurosciences, Cumming School of Medicine, Hotchkiss Brain Institute (J.M.O., J.K.H., A.G., S.B.C., B.M., M.D.H., M.G.), Foothills Medical Center, University of Calgary, AB, Canada.
Stroke. 2024 Nov;55(11):2742-2753. doi: 10.1161/STROKEAHA.123.044144. Epub 2024 Oct 22.
While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to frequentist approaches, which are based on the probability that data equal specific values given underlying unknown parameters, Bayesian approaches are based on the probability that parameters equal specific values given observed data and prior beliefs. The Bayesian paradigm allows researchers to update their beliefs with observed data to provide probabilistic interpretations of key parameters, for example, the probability that a treatment is effective. In this review, we outline the basic concepts of Bayesian statistics as they apply to stroke trials, compare them to the frequentist approach using exemplary data from a randomized trial, and explain how a Bayesian analysis is conducted and interpreted.
尽管大多数中风研究人员使用频率统计学来分析和呈现他们的数据，但贝叶斯统计学在中风研究中越来越流行。与基于给定潜在未知参数下数据等于特定值的概率的频率统计学方法相反，贝叶斯方法基于给定观测数据和先验信念下参数等于特定值的概率。贝叶斯范例允许研究人员用观测数据更新他们的信念，以提供关键参数的概率解释，例如，治疗有效的概率。在这篇综述中，我们概述了贝叶斯统计学的基本概念，因为它们适用于中风试验，并用来自随机试验的示例数据将它们与频率统计学方法进行比较，并解释了如何进行和解释贝叶斯分析。
