Group Iterative Multiple Model Estimation Approaches in Clinical Science

Psychological processes are highly heterogeneous, even among individuals with the same diagnosis. This variability poses challenges for nomothetic approaches that assume everyone is guided by the same broad psychological principles. In contrast, idiographic approaches focus on within-person variability but are often prone to noise and spurious relations and may not translate easily to clinical use due to limited generalizability. These constraints have motivated integrative approaches designed to model person-specific dynamics while still drawing on patterns that generalize across people. In this article, we review group iterative multiple model estimation (GIMME), one of the most widely used integrative approaches for modeling intensive longitudinal data (ILD) in clinical research. GIMME estimates person-specific dynamics using majority-shared paths as the backbone of individual models. We begin by introducing GIMME's core algorithm and its major extensions. We then review simulation studies evaluating its performance, survey empirical applications in clinical psychology, and outline alternative ILD methods. Finally, we discuss current limitations of GIMME and propose directions for its continued refinement.