Authors of a review explored the impact of major depressive disorder (MDD) on college students and the need for more research to evaluate risk factors for developing depressive symptoms. The authors discuss the utility of random-effects machine learning modeling in clinical science, especially for analyzing longitudinal data in heterogeneous disorders like depression. Linear mixed effects modeling, a common approach for longitudinal data, has limitations, including parametric assumptions and difficulty testing complex interactions. Random effects machine learning models, such as regression trees, are beneficial for identifying subgroups and producing user-friendly outputs that aid in clinical assessment and intervention.

These models, which include mixed effects random forest and RE-EM trees, can handle large datasets and automatically detect interactions between variables, offering a robust alternative to traditional mixed effects models. These techniques are useful for identifying critical risk factors and tailoring interventions for individuals at different risk levels for depression.

Reference: Bhaumik R, Stange J. Utilizing Random Effects Machine Learning Algorithms for Identifying Vulnerability to Depression. J Dep Anxiety. 2023; 12:516. doi: 10.35248/2167-1044.23.12.516.