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

Case conceptualization informs clinical decision making by generating hypotheses about predisposing, precipitating, and maintaining factors that are continually updated with new information from ongoing treatment. This study applies a novel data-driven approach to formalize this process with personalized network estimation through prior elicitation and Bayesian inference. It is the first study to assess the clinical utility of this approach in a sample of twelve psychotherapy patients, primarily treated for depression, along with their respective therapists.Patients employed the PECAN (Perceived Causal Networks) method to create personalized "prior networks," mapping how they perceived their symptoms to interact. Intensive longitudinal data were then collected six times daily over 15 days (N = 935). Bayesian inference was used to update these prior networks using the collected longitudinal data, resulting in personalized "posterior networks."Both PECAN and longitudinal assessments were evaluated feasible and acceptable. The posterior networks scored highest in face validity. Patients emphasized the personal relevance of these networks, while therapists noted their value in guiding the therapeutic process. However, prior, posterior, and data-driven 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 these discrepancies, this study demonstrates the potential for integrating two methods to create personalized networks. Future research should refine this formalization process to develop more robust, hypothesis-driven strategies that improve case conceptualization.