Latent Variable Approaches for Understanding Heterogeneity in Depression: A Dissertation

Background: Major depression is one of the most prevalent, disabling, and costly illnesses worldwide. Despite a 400% increase in antidepressant medication use since 1988, fewer than half of treated depression patients experience a clinically meaningful reduction in symptoms and uncertainty exists regarding how to successfully obtain symptom remission. Identifying homogenous subgroups based on clinically observable characteristics could improve the ability to efficiently predict who will benefit from which treatments. Methods: Latent class analysis and latent transition analysis (LTA) were applied to data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to explore how to efficiently identify subgroups comprised of the multiple dimensions of depression and examine changes in subgroup membership during treatment. The specific aims of this dissertation were to: 1) evaluate latent depression subgroups for men and women prior to antidepressant treatment; 2) examine transitions in these subgroups over 12 weeks of citalopram treatment; and 3) examine differences in functional impairment between women’s depression subgroups throughout treatment. Results: Four subgroups of depression were identified for men and women throughout this work. Men’s subgroups were distinguished by depression severity and psychomotor agitation and retardation. Severity, appetite changes, insomnia, and psychomotor disturbances characterized women’s subgroups. Psychiatric comorbidities, especially anxiety disorders, were related to increased odds of membership in baseline moderate and severe depression subgroups for men and women. After 12 weeks of citalopram