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

Statistical innovations allow clinicians to estimate personalized networks from longitudinal data, for example data collected via the Experience Sampling Method (ESM). Such networks can generate insights that may be relevant for constructing case formulations, and therefore guide the selection of personalized treatment targets. While the notion of personalized networks aligns well with the way clinicians think and reason, there are currently several barriers to clinical implementation that limit the utility of such models. First, the most popular network estimation routines are data-driven and do not allow clinicians to incorporate their expertise and theory. Second, network models have many parameters, which can make accurate estimation challenging. Finally, network estimation requires technical skills that are not regularly taught in clinical programs. In this article, we introduce PREMISE, an approach that formally integrates case formulations with personalized network estimation. Using prior elicitation techniques, clinical working hypotheses are translated into formal models, which can subsequently inform network estimation from ESM data using Bayesian inference. PREMISE tackles the three challenges described above: Incorporating clinical information into network estimation systematically allows theoretical and data-driven integration, which in turn increases the accuracy of network estimation techniques. In addition, we implemented the principles of PREMISE into a practical web-based toolkit that generates intuitive feedback, thereby facilitating clinical implementation. To illustrate its clinical potential, we use PREMISE to estimate clinically informed networks for a client suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.