Updating patient perceptions with intensive longitudinal data for enhanced case conceptualizations: An approach with Bayesian informative priors.

Abstract

Addressing the persistent heterogeneity in psychopathology, treatment outcomes, and the science-practice gap requires a systematic approach to personalizing psychotherapy. Case conceptualization seeks to understand a patient's unique psychopathology by generating and continuously updating hypotheses about predisposing, precipitating, and maintaining factors. This study introduces a new data-driven method to formalize this process with personalized network estimation, combining prior elicitation and Bayesian inference. It is the first to test its clinical usefulness with 12 patients, primarily treated for depression, and their therapists (preregistered and can be found as the additional online materials: https://osf.io/38qdx). Patients employed the Perceived Causal Networks method to create personalized "prior networks," mapping how they perceived their symptoms to interact. Bayesian inference was used to update these prior networks using longitudinal data collected subsequently 6 times daily over 15 days (N = 935), resulting in personalized "posterior networks." Both Perceived Causal Networks and longitudinal assessments were evaluated as feasible and acceptable. Face validity was scored highest for the posterior networks. Patients emphasized the personal relevance of these networks, while therapists noted their value in guiding the therapeutic process. However, prior, posterior, and data networks showed significant dissimilarities. These differences may stem from patients' limited insight into symptom interactions, insufficient power in the longitudinal data, or variations in self-perception. Despite some inconsistencies, the study shows potential for combining two methods to create personalized models of psychopathology, highlighting the need for future research to refine this formalization process into a more rigorous theoretical-empirical cycle to test these models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).