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Abstract:
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While multiple sclerosis (MS) patients commonly experience depressive symptoms, clinicians cannot reliably distinguish the mediating pathways through which different trajectories of MS leads to depression over time. In this talk, a longitudinal structural equation modeling (SEM)-based approach will be combined with latent growth modeling (LGM). This approach will be used for examining the hypothesized mechanism, functional disability, through which MS, as defined by type and baseline time since diagnosis, leads to depression using the Knowledge Program (KP) at the Cleveland Clinic's Neurological Institute data base. SEM is a very general technique combining complex path models with latent (unobserved) variables. LGM is a practical application of SEM for longitudinal data to estimate growth trajectory, analogous to mixed effects modeling in more traditional analyses. The KP links patient-reported depression (via the PHQ-9) responses to the EPIC EHR and provides a powerful opportunity to study and improve patient care and clinical research. SEM is a very appropriate approach to handing the latent variables, causality questions and irregular follow-up times in the KP data base.
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