Nimrod Hertz-Palmor, Alicja Podgorski, Gary Brown, Amit Lazarov, Jana Porat, Shany Edelman Yaron, Anna Bevan, Tim Dalgleish
We demonstrate a method for bridging individualised assessment with theory-driven analysis. By combining personalised data collection with a shared mechanistic framework, idiographic networks can extend beyond single-case descriptions to identify common clinical patterns, with potential implications for formulation, intervention planning, and evaluation.
BACKGROUND: Psychopathology research typically relies on nomothetic (group-level) data to infer idiographic (person-level) processes, limiting clinical relevance and failing to capture complex individual presentations. Conversely, idiographic approaches capture within-person dynamics but often lack theoretical structure to support generalisation, creating a nomothetic-idiographic gap. This study addresses this gap by operationalising Ehlers and Clark's (E&C) Cognitive Model of Posttraumatic Stress Disorder (PTSD) within a theory-guided idiographic network framework.
METHOD: Study 1 used simulations to establish data requirements for reliably testing model-derived hypotheses. Study 2 applied this framework to intensive longitudinal data from trauma-exposed individuals. Fifty-five participants (31 with PTSD, 24 with clinical-level symptoms) selected personalised items reflecting core model components: negative appraisals, trauma memory, triggers, sense of current threat, and maladaptive strategies. A subsample of 41 participants completed 3 weeks of Ecological Momentary Assessment, yielding 2,594 observations (M=63.2 per participant). Individualised items were mapped onto shared theoretical constructs and analysed using complementary network models.
RESULTS: Findings provided empirical support for key assumptions of the E&C model, particularly the central role of sense of current threat and its associations with trauma memory, negative appraisals, triggers, and maladaptive strategies. Clustering of individual networks identified four recurring formulation subtypes characterised by threat reactivity, appraisal centrality, memory centrality, or trigger-threat association.
CONCLUSIONS: We demonstrate a method for bridging individualised assessment with theory-driven analysis. By combining personalised data collection with a shared mechanistic framework, idiographic networks can extend beyond single-case descriptions to identify common clinical patterns, with potential implications for formulation, intervention planning, and evaluation.