(PS12-A8) Changes in Youth Posttraumatic Stress Symptoms Structure After Completing Trauma Focused Cognitive Behavior Therapy: A Network Approach

Trauma-focused cognitive behavioral therapy (TF-CBT) is an evidence-based treatment widely used for youth who have experienced trauma. However, symptom structure changes after completing TF-CBT are not well understood. To address this gap, the study proposes to use a novel approach, temporal network analysis, to examine how the structure of PTSS changes over time after completing TF-CBT. Instead of viewing PTSD as an underlying latent construct, this approach considers PTSD as a result of complex causal interactions among symptoms. A network includes nodes (i.e., the observed symptoms) and edges (i.e., the connections among symptoms), and can demonstrate the relative importance of symptoms in the network by the location and the strengths of the network. 

Using data from a state-wide implementation of TF-CBT across 15 years in a midwest state, the current study proposes to use network analysis to examine how PTSS structure changes throughout TF-CBT among trauma-exposed youth. Participants were 2006 (1141 girls, Mage = 11.93, 49.9% youth of color) treatment-seeking youths with trauma exposure. UCLA Child/Adolescent PTSD Reaction Index was administered every three months to monitor PTSS. 

First, two PTSS networks will be estimated at baseline and post-treatment using the Gaussian Graphical Model (EBICGlasso) in the R qgraph package. Then, Network Comparisons Tests (NCT) will be conducted to compare general network strength connectivity between pre-and post-treatment. Then, a temporal dynamic PTSS network will be estimated using the multilevel vector autoregressive (mlVAR) model in R mlVAR package. The mlVAR model can simultaneously estimate temporal (i.e., whether and how symptoms at timet-1 can predict future symptoms at timet), contemporaneous (i.e., whether and how symptoms can predict one another in the same time window), and between-person relationships (i.e., whether and how person-mean of one variable can be predicted by the means of other variables). Centrality measures will also be estimated for all networks to determine which symptoms are more important. 

The study's findings can help researchers and clinicians better understand how TF-CBT reduces PTSS. It can also inform the development of more effective and efficient treatment strategies by identifying key symptoms that should be prioritized during treatment. Overall, the proposed study has the potential to make a significant contribution to the field of trauma-focused interventions for youth.