Robustness, generalizability, and heterogeneity of dynamic networks of psychopathology.

The network perspective of psychopathology proposes that mental disorders arise from dynamic interactions between psychopathology-relevant variables. This study explored the robustness, generalizability, and heterogeneity of dynamic networks of psychopathology using Ecological Momentary Assessment data of 173 participants. Robustness - i.e., how precisely model parameters are estimated - of nomothetic networks was assessed via case-dropping bootstrapping. Translatability (i.e., how much group-derived estimates reflect individual processes) was evaluated by comparing freely estimated idiographic networks to idiographic networks where significant effects from the nomothetic network were constrained. Heterogeneity was analyzed using the Individual Network Invariance Test per pair of individuals. Results suggest that robustness was acceptable overall. Translatability from nomothetic to idiographic networks was limited, and inter-individual heterogeneity was large. The limited generalizability and large heterogeneity shows the urgency of finding homogeneous groups. Recommendations to find such groups combining data-driven and theory-driven approaches with a focus on single-case research are discussed.