Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis.

ABSTRACT:

Background

Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at multiple levels (persons, symptoms, time) and LVMs cannot capture all these levels and their interactions simultaneously, which leads to incomplete models. Our objective is to briefly review the most widely used LVMs in depression research, illustrating their use and incompatibility in real data, and to consider an alternative, statistical approach, namely multimode principal component analysis (MPCA).

Methods

We applied LVMs to data from 147 patients, who filled out the Quick Inventor

SUBMITTER: de Vos S 

PROVIDER: S-EPMC4608190 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature