Department of Neurology, University of Utah School of Medicine.
Department of Surgery, Division of Trauma and Acute Care Surgery, Medical College of Wisconsin.
Neuropsychology. 2023 May;37(4):398-408. doi: 10.1037/neu0000823. Epub 2022 Jul 7.
The variety of instruments used to assess posttraumatic stress disorder (PTSD) allows for flexibility, but also creates challenges for data synthesis. The objective of this work was to use a multisite mega analysis to derive quantitative recommendations for equating scores across measures of PTSD severity.
Empirical Bayes harmonization and linear models were used to describe and mitigate site and covariate effects. Quadratic models for converting scores across PTSD assessments were constructed using bootstrapping and tested on hold out data.
We aggregated 17 data sources and compiled an = 5,634 sample of individuals who were assessed for PTSD symptoms. We confirmed our hypothesis that harmonization and covariate adjustments would significantly improve inference of scores across instruments. Harmonization significantly reduced cross-dataset variance (28%, < .001), and models for converting scores across instruments were well fit (median ² = 0.985) with an average root mean squared error of 1.46 on sum scores.
These methods allow PTSD symptom severity to be placed on multiple scales and offers interesting empirical perspectives on the role of harmonization in the behavioral sciences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
用于评估创伤后应激障碍（PTSD）的各种工具具有灵活性，但也给数据综合带来了挑战。本研究的目的是使用多站点荟萃分析，为 PTSD 严重程度的各种测量方法之间的分数等化提供定量建议。
采用经验贝叶斯协调和线性模型来描述和减轻站点和协变量的影响。使用自举法构建 PTSD 评估分数转换的二次模型，并在保留数据上进行测试。
我们汇总了 17 个数据源，并对 5634 名 PTSD 症状评估个体进行了分析。我们证实了我们的假设，即协调和协变量调整将显著改善跨工具的分数推断。协调显著降低了跨数据集的方差（28%，<0.001），并且跨工具转换分数的模型拟合良好（中位数 ² = 0.985），总分的平均均方根误差为 1.46。
这些方法允许 PTSD 症状严重程度被置于多个量表上，并为协调在行为科学中的作用提供了有趣的实证视角。
