Analyzing Chronic Diseases with Latent Growth Models: An Analysis of Multiple Sclerosis

Abstract

Evidence based decision making in the context of chronic disease management requires long term tracking and analysis of patient data. This study demonstrates how disease data tracking can help in understanding underlying patterns in chronic disease progression. Latent Growth Modeling (LGM) is used as a tool to analyze the long term chronic data related to the progression of Multiple Sclerosis (MS). The survey data has been collected on a bi-annual basis by the North American Research Committee on Multiple Sclerosis (NARCOMS), a project of the Consortium of Multiple Sclerosis Centers for the purpose of clinical trial recruitment and epidemiological research. This data set allows for study of MS progression, by measuring three base models: Patient Determined Disease Steps (PDDS), Overall Health and Emotional Health. MS patient data are grouped as early, middle and late disease status. This study analyzes three temporal data points spanning three years and identifies patient traits that are both patient and physician controlled. Empirical evidence confirms many practitioner observations.