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. 2023 May;37(4):398-408.
doi: 10.1037/neu0000823. Epub 2022 Jul 7.

Harmonizing PTSD severity scales across instruments and sites

Eamonn Kennedy  1 , Emily L Dennis  1 , Hannah M Lindsey  1 , Terri deRoon-Cassini  2 , Stefan Du Plessis  3 , Negar Fani  4 , Milissa L Kaufman  5 , Nastassja Koen  3 , Christine L Larson  6 , Sarah Laskowitz  7 , Lauren A M Lebois  5 , Rajendra A Morey  7 , Mary R Newsome  8 , Cori Palermo  5 , Nicholas J Pastorek  8 , Abigail Powers  4 , Randall Scheibel  8 , Soraya Seedat  9 , Antonia Seligowski  5 , Dan J Stein  3 , Jennifer Stevens  4 , Delin Sun  7 , Paul Thompson  10 , Maya Troyanskaya  8 , Sanne J H van Rooij  4 , Amanda A Watts  7 , Carissa W Tomas  11 , Wright Williams  12 , Frank G Hillary  13 , Mary Jo Pugh  1 , Elisabeth A Wilde  1 , David F Tate  1
Affiliations

Harmonizing PTSD severity scales across instruments and sites

Eamonn Kennedy et al. Neuropsychology. 2023 May.

Abstract

Objective: 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.

Method: 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.

Results: We aggregated 17 data sources and compiled an n = 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%, p < .001), and models for converting scores across instruments were well fit (median R² = 0.985) with an average root mean squared error of 1.46 on sum scores.

Conclusions: 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).

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Figures

Figure 1:
Figure 1:. Summary of PTSD symptom reporting across all datasets.
(a) A histogram illustrates the distribution of all raw percentage severity sum scores. (b) Percentage severity sum scores are shown broken out as boxplots by military status and sex/gender. (c) A stem plot shows the mean severity reported for each item. (c, right) The average differences between military and civilian severity scores are shown for each item. (* indicates significance at p<0.05 after q correction for multiple comparisons).
Figure 2:
Figure 2:. Visualizing raw severity scores across datasets and instruments.
(a) The sorted severity scores of datasets that used the same instrument are compared. The gray line indicates equality from the lowest to the highest possible score on each scale. The coefficients of determination (R2, inset) measure deviation from the line. (b) Like (a) but comparing datasets that used different PTSD assessment instruments.
Figure 3:
Figure 3:. Comparison of pre/post harmonization fit quality.
(a) The distribution of coefficients of determination are shown as boxplots broken out by data source. (b) Like (a) but after Bayesian correction of site effects.
Figure 4:
Figure 4:. Converting severity scores.
Figure 4a plots model-inferred PTSD severity scores on different instruments as a function of real PCL-5 severity scores. The shaded areas indicate ±1 RMSE. Figure 4b shows the equivalent PCL-5 severity score distributions after conversion.
See this image and copyright information in PMC

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