Keywords: Comorbidity | Adult Anxiety
Presentation Type: Symposium
Network analysis has recently become increasingly popular in psychopathology research (Borsboom & Cramer, 2013). Network models appeal to the idea that psychopathology is not as neat and tidy as the traditional disease model would have us believe. Network analysis provides tools for digitizing complex sets of relationships in a relatively parsimonious way – including ways to model and understand comorbidity. Importantly, the overwhelming majority of the work in this area has been limited to nomothetic modeling of cross-sectional data. Multivariate time series data – the intensive repeated measurement of several variables within individuals – provides the opportunity to harness these models for examining the dynamics of psychopathology within individual participants. However, time series data provide another challenge that has not been addressed in the literature: the choice between concurrent and time-lagged models. No methodology to date has incorporated contemporaneous and time-lagged effects for evaluating network flow – the transmission of information through a network. The proposed talk will present a network model that integrates contemporaneous and lagged associations in mood and anxiety symptomatology on a person by person basis. The proposed model concurrently estimates all contemporaneous and lagged associations in a single multivariate structural equation model. Data were taken from 40 individuals with generalized anxiety disorder and/or major depressive disorder who answered questions about 21 descriptors of mood and anxiety symptomatology four times a day over a period of at least 30 days. The model provided an excellent fit the intraindividual symptom dynamics of all 40 individuals. Examples will be provided to illustrate the application and interpretation of these models.
University of California, Berkeley
Thursday, November 16
8:30 AM – 12:30 PM
Saturday, November 18
1:45 PM – 3:15 PM
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