A Gaussian Graphical Model (GGM, i.e. a partial correlation network) was estimated for the empirical data using Spearman correlations between symptoms (Epskamp & Fried, 2018). Missing data was handled using pairwise estimation. The gLASSO regularization was applied with a tuning parameter of 0.5 and the Extended Bayesian Information Criterion (EBIC) (Chen & Chen, 2008; Friedman, Hastie, & Tibshirani, 2008). The strength and expected influence of each symptom were considered. Finally, the stability of the network parameters and centrality indices were calculated using bootstrapping methods (Epskamp, Borsboom, & Fried, 2018).

Based on clinicians’ PSR, one network per clinician was constructed (four in total). Clinicians’ indications on the strength of the relation between a symptom pair were used as edges weights (‘do not relate’ = 0, ‘relate weakly positive/negative’ = (−)0.2, ‘relate positive/negative’ = (−)0.5, ‘relate strongly positive/negative’ = (−)0.7, ‘relate very strongly positive/negative’ = (−)1). Through averaging edge weights for each symptom pair an average PSR-network was created. Strength and expected influence of each symptom were investigated for all PSR-networks. To assess the similarity of the individual PSR-networks, the edge weights and centrality indices (strength and expected influence) of all PSR-networks were correlated with each other. Further, to inspect the similarity of the general structure of these networks, we assessed the number of edges on which the individual PSR-networks agreed regarding the presence or absence of the edge.

The empirical network and the PSR-networks were compared in several analyses. First, edge weights of the PSR-networks were correlated with edge weights of the empirical network (indicating partial correlations). Second, edge weights of the PSR-networks were correlated with the symptom correlations of the empirical data. Third, to compare their general structure, we looked at the agreement regarding the presence versus absence of an edge between the empirical and the PSR-network. Here, the overlap between edges was explored 1) for the empirical network and the average PSR-network including all edges and 2) for the empirical network and the average PSR-network only including edges, with a weight larger than 0.5. Finally, the empirical data were fit to the binary adjacency matrix of each PSR-network (indicating the presence or absence of an edge) using the R packages psychonetrics (Epskamp, 2020) and the model fit of each PSR structure was investigated. All analyses were done in R 3.6.1. Code for the complete analysis can be found here https://osf.io/hkdrb/?view_only=b9a7f04dc9684a45a1bed9d1c119b5dd.

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