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

Abstract

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. 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. 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. We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.