From the Departments of Biostatistics and Anesthesia, Clinical Trials Statistical and Data Management Center, University of Iowa, Iowa City, Iowa.
Department of Biostatistics, University of Iowa College of Public Health, Iowa City, Iowa.
Anesth Analg. 2024 Mar 1;138(3):530-541. doi: 10.1213/ANE.0000000000006696. Epub 2024 Feb 16.
Bayesian analyses are becoming more popular as a means of analyzing data, yet the Bayesian approach is novel to many members of the broad clinical audience. While Bayesian analyses are foundational to anesthesia pharmacokinetic/pharmacodynamic modeling, they also can be used for analyzing data from clinical trials or observational studies. The traditional null hypothesis significance testing (frequentist) approach uses only the data collected from the current study to make inferences. On the other hand, the Bayesian approach quantifies the external information or expert knowledge and combines the external information with the study data, then makes inference from this combined information. We introduce to the clinical and translational science researcher what it means to do Bayesian statistics, why a researcher would choose to perform their analyses using the Bayesian approach, when it would be advantageous to use a Bayesian instead of a frequentist approach, and how Bayesian analyses and interpretations differ from the more traditional frequentist methods. Throughout this paper, we use various pain- and anesthesia-related examples to highlight the ideas and statistical concepts that should be relatable to other areas of research as well.
贝叶斯分析作为一种数据分析方法正变得越来越流行，但对于广大临床受众中的许多成员来说，贝叶斯方法是新颖的。虽然贝叶斯分析是麻醉药代动力学/药效学建模的基础，但它们也可用于分析临床试验或观察性研究的数据。传统的零假设显著性检验（频率主义）方法仅使用当前研究中收集的数据进行推断。另一方面，贝叶斯方法量化了外部信息或专家知识，并将外部信息与研究数据结合起来，然后从这些综合信息中进行推断。我们向临床和转化科学研究人员介绍了进行贝叶斯统计的含义、研究人员为什么选择使用贝叶斯方法进行分析、何时使用贝叶斯方法而不是频率主义方法会有优势，以及贝叶斯分析和解释与更传统的频率主义方法有何不同。在整篇论文中，我们使用各种与疼痛和麻醉相关的例子来突出应该与其他研究领域相关的思想和统计概念。
