层次贝叶斯模型在实验精神病理学数据中的应用:介绍与教程。

IF 4.6 1区 心理学 Q1 Medicine
Ivy F Tso, Stephan F Taylor, Timothy D Johnson
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引用次数: 3

摘要

在过去的二十年中,贝叶斯方法在许多科学学科中越来越受欢迎。然而,到目前为止,他们很少是正式的研究生统计培训在临床科学的一部分。尽管贝叶斯方法可以作为经典方法的一个有吸引力的替代方法来回答某些研究问题,但它们涉及沉重的“开销”(例如,先进的数学方法,复杂的计算),这对有兴趣将贝叶斯方法添加到他们的统计工具箱中的研究人员构成了重大障碍。为了增加贝叶斯方法对精神病理学研究人员的可访问性,本文介绍了贝叶斯推理框架和实施教程。我们首先介绍贝叶斯推理的关键概念以及与贝叶斯估计相关的主要实现考虑因素。然后,我们演示了如何将层次贝叶斯建模(HBM)应用于实验精神病理学数据。使用从两个临床组(精神分裂症和双相情感障碍)收集的真实数据集和心理物理凝视感知任务的健康比较样本,我们说明了如何使用概率函数来模拟个人反应和群体差异,尊重假定的潜在数据生成过程和数据的层次性质。我们为代码提供了解释和用于生成和可视化结果的数据,以促进学习。最后,我们讨论了后验概率对结果的解释,并将结果与使用传统方法获得的结果进行了比较。(PsycInfo Database Record (c) 2021 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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来源期刊
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期刊介绍: The Journal of Abnormal Psychology® publishes articles on basic research and theory in the broad field of abnormal behavior, its determinants, and its correlates. The following general topics fall within its area of major focus: - psychopathology—its etiology, development, symptomatology, and course; - normal processes in abnormal individuals; - pathological or atypical features of the behavior of normal persons; - experimental studies, with human or animal subjects, relating to disordered emotional behavior or pathology; - sociocultural effects on pathological processes, including the influence of gender and ethnicity; and - tests of hypotheses from psychological theories that relate to abnormal behavior.
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