Detecting Specific Patterns of Change for Two Outcome Scores in a Mental Health Study by Means of Linear Mixture Models
Abstract
Common practice evaluates the success of any form of (medical) treatment by evaluating the change and the relation of change for particular outcome criteria. However, we must assume that the expected change over time can only be observed for—perhaps only small—subgroups. The purpose of this article, therefore, is to explain and demonstrate a method on how to measure the relationships of change for important outcomes in a longitudinal study of schizophrenia treatment and to identify latent subgroups of patients characterized by different shapes of trajectories and relations of trajectories. We will focus on the change over time for the Social and Occupational F unctioning A ssessment S core (SOFAS) (American Psychiatric Association, 1987) and the subjective health-related quality of life assessed by SF-36(Ware, Gandek, & The IQOLA Project Group, 1994). The sample employed for this study consists of 307 patients with a diagnosis of schizophrenia (ICD-10 F20.0) treated by psychiatric services in Leipzig. The method adopted to portray these changes is the growth mixture model, which combines latent class analysis with the approach of modeling individual trajectories. Results show that a positive relation between the outcomes of interest can only be observed for about 15% of the sample.
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