Toward evidence-based medical statistics. 2: The Bayes factor
Toward evidence-based medical statistics. 2: The Bayes factor
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DOI：
10.7326/0003-4819-130-12-199906150-00019
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发表时间：
1999-06-15
影响因子：
39.2
通讯作者：
Goodman, SN
中科院分区：
文献类型：
--
作者：
Goodman, SN
贝叶斯推理通常被认为是一种确定数据如何修改科学信念的方法。尽管贝叶斯方法论是过去 20 年来统计发展最活跃的领域之一，但医学研究人员一直不愿意接受他们认为的主观数据分析方法。人们很少了解贝叶斯方法有一个基于数据的核心，可以用作证据演算。这个核心是贝叶斯因子，其最简单的形式也称为似然比。最小贝叶斯因子是客观的，可以用来代替 P 值作为证据强度的度量。与 P 值不同，贝叶斯因子具有良好的理论基础和解释，允许将其用于推理和决策。贝叶斯因子表明 P 值大大夸大了反对原假设的证据。最重要的是，贝叶斯因素需要添加背景知识才能转化为推论——给定结论正确或错误的概率。它们明确区分了实验证据和推论结论，同时提供了一个将先前证据与当前证据相结合的框架。
Bayesian inference is usually presented as a method for determining how scientific belief should be modified by data. Although Bayesian methodology has been one of the most active areas of statistical development in the past 20 years, medical researchers have been reluctant to embrace what they perceive as a subjective approach to data analysis. It is little understood that Bayesian methods have a data-based core, which can be used as a calculus of evidence. This core is the Bayes factor, which in its simplest form is also called a likelihood ratio. The minimum Bayes factor is objective and can be used in lieu of the P value as a measure of the evidential strength. Unlike P values, Bayes factors have a sound theoretical foundation and an interpretation that allows their use in both inference and decision making. Bayes factors show that P values greatly overstate the evidence against the null hypothesis. Most important, Bayes factors require the addition of background knowledge to be transformed into inferences-probabilities that a given conclusion is right or wrong. They make the distinction clear between experimental evidence and inferential conclusions while providing a framework in which to combine prior with current evidence.