Will Woods (University of Pittsburgh)

An accident of the traditional nomothetic approach to psychopathological research has been the invention of “the Average Patient.” The Average Patient has been used to validate treatments, describe nomological networks among psychopathological phenomena, and generate models of hypothesized processes. However, the Average Patient has the unfortunate quality of never quite showing up for treatment. Instead, clinicians are presented with patients whose heterogenous clinical presentations do not neatly align with the treatment protocols developed through study of the Average Patient. Despite growing acceptance of the need for more personalized approaches to psychopathology research and treatment, researchers have been reluctant to abandon the Average Patient’s ability to generate generalizable knowledge. Group Iterative Multiple Model Estimation (GIMME) is an analytic tool that satisfies both needs through its ability to generate both person-specific and group-level models of longitudinal data. In this presentation, I will use a BPD sample to show the benefits of the GIMME’s person- and group-level analyses. Exemplar person-level models from the sample will be used to show the potential clinical benefits of these analytic techniques for treatment planning. I will then discuss how GIMME’s person- and group-level analyses are able to inform, and be informed, by the Average Patient’s steadfast contribution to our understanding of human emotional suffering.