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现代外科科学家的统计方法:频率主义与贝叶斯范式的冲突。

Modern Statistical Methods for the Surgeon Scientist: The Clash of Frequentist versus Bayesian Paradigms.

机构信息

Department of General Surgery, Madigan Army Medical Center, 9040 Jackson Avenue, Tacoma, WA 98431, USA.

Department of General Surgery, Madigan Army Medical Center, 9040 Jackson Avenue, Tacoma, WA 98431, USA.

出版信息

Surg Clin North Am. 2023 Apr;103(2):259-269. doi: 10.1016/j.suc.2022.12.001.

DOI:10.1016/j.suc.2022.12.001
PMID:36948717
Abstract

The practice of evidence-based medicine is the result of a multitude of research and trials aimed toward improving health-care outcomes. An understanding of the associated data remains paramount toward optimizing patient outcomes. Medical statistics commonly revolve around frequentist concepts that are convoluted and nonintuitive for nonstatisticians. Within this article, we will discuss frequentist statistics, their limitations, as well as introduce Bayesian statistics as an alternative approach for data interpretation. By doing so, we intend to highlight the importance of correct statistical interpretations through clinically relevant examples while providing a deeper understanding of the underlying philosophies of frequentist and Bayesian statistics.

摘要

循证医学的实践是大量旨在改善医疗保健结果的研究和试验的结果。对相关数据的理解仍然是优化患者结局的关键。医学统计通常围绕着对于非统计学家来说复杂且难以理解的频率主义概念。在本文中,我们将讨论频率主义统计学及其局限性,并介绍贝叶斯统计学作为数据解释的替代方法。通过这样做,我们旨在通过临床相关示例强调正确统计解释的重要性,同时深入了解频率主义和贝叶斯统计学的基本原理。

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