Network models of posttraumatic stress disorder: A meta-analysis.

Abstract

Posttraumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account and applied this framework in the first-ever meta-analytic study of PTSD symptom networks. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561 and estimated a single pooled network model underlying the data sets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (a) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (b) While several clear symptom-links, interpretable clusters, and significant differences between strength of edges and centrality of nodes can be identified in the network, no single or small set of nodes that clearly played a more central role than other nodes could be pinpointed, except for the symptom "amnesia" that was clearly the least central symptom. (c) Despite large between-study heterogeneity, we found that network models estimated from single samples can lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics. General Scientific Summary In this meta-analysis, we used new statistical routines to analyze 52 data sets used in publications on posttraumatic stress symptoms (PTSS) networks. We find a generalizable network structure that contains the most salient symptom-interactions, but also identify a large degree of heterogeneity across these data sets, indicating that data sets can vastly differ from one-another. We discuss both clinical and methodological implications. Important for PTSS treatment, our meta-analysis indicates that we should not expect a single PTSS symptom to drive other symptoms.