A clinical PREMISE for personalized models: Toward a formal integration of case formulations and statistical networks.

Abstract

Over the past decade, the idiographic approach has received significant attention in clinical psychology, incentivizing the development of novel approaches to estimate statistical models, such as personalized networks. Although the notion of such networks aligns well with the way clinicians think and reason, there are currently several barriers to implementation that limit their clinical utility. To address these issues, we introduce the Prior Elicitation Module for Idiographic System Estimation (PREMISE), a novel approach that formally integrates case formulations with personalized network estimation via prior elicitation and Bayesian inference. PREMISE tackles current implementation barriers of personalized networks; incorporating clinical information into personalized network estimation systematically allows theoretical and data-driven integration, supporting clinician and patient collaboration when building a dynamic understanding of the patient’s psychopathology. To illustrate its potential, we estimate clinically informed networks for a patient suffering from obsessive–compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.

For living well, behaviors and circumstances matter just as much as psychological traits

“…61 ). Additionally, although the findings do suggest that using self-rated personality traits (or any single method measures) as customization targets may lack diversity and risk conflating survey behaviors with the underlying traits themselves—to our knowledge—personalized interventions in psychology typically either allow substantial participant agency through partly self-tailored interventions ( 21 , 62 ), which would not be constrained by closed-ended response styles, or leverage more complex within-person modeling approaches ( 63 ), which would also not tend to be sensitive to the cross-individual variation studied here. Instead, open-ended questions and preregistered measures in interventions, and measures that do not rely on training or codebook creation data from a pretest, could eventually be informative additions to experimental work and in largely the same ways that they would add diversity to longitudinal studies.…”

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data

“…Typical EMA surveys include self-reported moods, thoughts, behaviors, context variables/situational factors, and symptoms (Ebner-Priemer & Trull, 2009 ). EMA has been used in various settings, including observational (see, e.g., Shiffman et al, 2008 ), interventional research (Henry et al, 2022 ), and to collect data used for personalized feedback in clinical settings (Bringmann et al, 2021 ; Burger et al, 2022 ; von Klipstein et al, 2023 ).…”

Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

“…Network analysis can assist in examining the interrelatedness of these processes to recognize causal relationships, quantify preliminary analysis, and finalize the conceptual functional analysis (see S. C. Hayes, Hofmann, Hayes et al, 2020b for additional information about functional analysis). The systems approach facilitates the integration of clinical knowledge and databased approaches, which could ultimately lead to case formulations with personalized network assessment (e.g., Burger et al, 2022) and targeting empirically established processes that are functionally important to the individual (Ong et al, 2022). Importantly, the systems approach does not contradict existing concepts of mental disorders but rather provides impetus for the integration and expansion of existing approaches.…”