journal article May 09, 2020

The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?

Journal of Clinical Psychology Vol. 76 No. 9 pp. 1591-1612 · Wiley
View at Publisher Save 10.1002/jclp.22957
Abstract
AbstractObjectiveNetwork analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically‐relevant longitudinal data.MethodsWe provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points.ResultsThe following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations.ConclusionWe conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.
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Published
May 09, 2020
Vol/Issue
76(9)
Pages
1591-1612
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Cite This Article
D. Gage Jordan, E. Samuel Winer, Taban Salem (2020). The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?. Journal of Clinical Psychology, 76(9), 1591-1612. https://doi.org/10.1002/jclp.22957
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