Mixture Model Applications in Depression Phenotyping: Practices, Challenges, and Recommendations

Applications of mixture models are prevalent in studying psychopathology across development, particularly for identifying typical co-occurring symptom presentations (or phenotypes) in depression. Researchers has used both longitudinal and cross-sectional with varied statistical methods. The current study focused on studies that applied latent profile analysis, latent class growth analysis, growth mixture models to phenotype continuously treated depressive symptoms. The current study aims to (a) provide a brief overview of common mixture models that are used in depression phenotyping, (b) review empirical applications of these methods in cross-sectional and longitudinal research of depression, (c) discuss the methodological considerations and recommendations in identifying phenotypes of depression when continuously treated symptoms are used. In 72 studies, we found heterogeneity in mixture model specification, selection, and interpretation. We identified three challenges in current practices: a “garbage in, garbage out” problem, inconsistent use and reporting of model selection criteria, and diverse, incomparable, and incomplete phenotype characterizations. We recommend: (1) in model specification, select and justify measures and models based on research question, (2) in model comparison, report BIC and bootstrapped likelihood ratio tests of all compared models and ground model selection on philosophy of science, (3) in model interpretation, provide all parameter estimates and use R2 measures for class characterization.