Department of Clinical Biochemistry, North West London Pathology, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London, UK.
Clin Chem. 2022 Jul 3;68(7):893-905. doi: 10.1093/clinchem/hvac049.
Statistical analyses form a fundamental part of causal inference in the experimental sciences. The statistical paradigm most commonly taught to science students around the world is that of frequentism, with a particular emphasis on the null hypothesis significance testing borne by the work of Neyman and Pearson in the early 20th century. This paradigm is often lauded as being the most objective of methods and remains commonplace in scientific journals. Despite its widespread use-and, indeed, requirement for publication in some journals-this paradigm has received substantial criticism in recent decades, and its impact on scientific publishing has been subjected to deeper scrutiny in response to the replication crisis in the psychological and medical sciences. It has been posited that the increasing use of the Bayesian statistical paradigm, made more accessible through technological advances in the last few decades, may have an important role to play in rendering research and statistical inference more robust, transparent, and reproducible. These methods can have a steep learning curve, and thus this paper seeks to introduce those working within clinical laboratories to the Bayesian paradigm of statistical analysis and provides worked examples of the Bayesian analysis of data commonly encountered in laboratory medicine using freely available, open source tools.
统计分析是实验科学中因果推断的基础部分。全世界的科学学生最常学习的统计范式是频率主义，特别强调 20 世纪早期由 Neyman 和 Pearson 提出的零假设显著性检验。这种范式通常被称赞为最客观的方法，并且在科学期刊中仍然很常见。尽管这种范式被广泛使用——实际上，在某些期刊中，它是发表的要求——但近几十年来，它受到了大量的批评，并且随着心理学和医学科学中的复制危机，它对科学出版的影响受到了更深入的审查。有人提出，随着过去几十年技术的进步，贝叶斯统计范式的使用越来越多，可能在使研究和统计推断更加稳健、透明和可重复方面发挥重要作用。这些方法可能有一个陡峭的学习曲线，因此本文旨在向临床实验室的工作人员介绍贝叶斯统计分析范式，并使用免费提供的开源工具，提供在实验室医学中常见的数据贝叶斯分析的实例。
