Premium
The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?
Author(s) -
Jordan D. Gage,
Winer E. Samuel,
Salem Taban
Publication year - 2020
Publication title -
journal of clinical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.124
H-Index - 119
eISSN - 1097-4679
pISSN - 0021-9762
DOI - 10.1002/jclp.22957
Subject(s) - context (archaeology) , structural equation modeling , network analysis , computer science , data science , software , sample (material) , code (set theory) , data mining , machine learning , artificial intelligence , psychology , physics , quantum mechanics , paleontology , chemistry , set (abstract data type) , chromatography , biology , programming language
Objective Network 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. Methods We 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. Results The 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. Conclusion We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.