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特邀评论:多重检验的贝叶斯推断

Invited Commentary: Bayesian Inference with Multiple Tests.

作者信息

Jewsbury Paul A

机构信息

Educational Testing Service, Foundational Psychometric and Statistical Research, 660 Rosedale Rd, M/s T-02, Princeton, NJ, 08541, USA.

出版信息

Neuropsychol Rev. 2023 Sep;33(3):643-652. doi: 10.1007/s11065-023-09604-4. Epub 2023 Aug 18.

Abstract

Dr. Leonhard presents a comprehensive and insightful critique of the existing malingering research literature and its implications for neuropsychological practice. Their statistical critique primarily focuses on the crucial issue of diagnostic inference when multiple tests are involved. While Leonhard effectively addresses certain misunderstandings, there are some overlooked misconceptions within the literature and a few new confusions were introduced. In order to provide a balanced commentary, this evaluation considers both Leonhard's critiques and the malingering research literature. Furthermore, a concise introduction to Bayesian diagnostic inference, utilizing the results of multiple tests, is provided. Misunderstandings regarding Bayesian inference are clarified, and a valid approach to Bayesian inference is elucidated. The assumptions underlying the simple Bayes model are thoroughly discussed, and it is demonstrated that the chained likelihood ratios method is an inappropriate application of this model due to one reason identified by Leonhard and another reason that has not been previously recognized. Leonhard's conclusions regarding the primary dependence of incremental validity on unconditional correlations and the alleged mathematical incorrectness of the simple Bayes model are refuted. Finally, potential directions for future research and practice in this field are explored and discussed.

摘要

莱纳德博士对现有的诈病研究文献及其对神经心理学实践的影响进行了全面且有深刻见解的批判。他们的统计批判主要聚焦于涉及多项测试时诊断推断的关键问题。虽然莱纳德有效地解决了某些误解,但文献中仍存在一些被忽视的错误观念,并且还引入了一些新的混淆之处。为了提供一个平衡的评论,本评估既考虑了莱纳德的批判,也考虑了诈病研究文献。此外,还提供了一个利用多项测试结果对贝叶斯诊断推断的简要介绍。澄清了关于贝叶斯推断的误解,并阐明了一种有效的贝叶斯推断方法。深入讨论了简单贝叶斯模型的基础假设,并表明由于莱纳德指出的一个原因以及另一个此前未被认识到的原因,连锁似然比方法是该模型的不当应用。反驳了莱纳德关于增量效度主要依赖无条件相关性以及简单贝叶斯模型所谓数学错误的结论。最后,探讨并讨论了该领域未来研究和实践的潜在方向。

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