A Longitudinal Approach to Understanding the Relationship Between Symptom Status and QOL

This study demonstrates how a variant of growth curve modeling known as longitudinal parallel-process modeling can yield an understanding of the effect of symptoms on quality of life (QOL). A two-level hierarchical linear model with random intercepts and slopes was implemented within a structural equation modeling approach. The data (N = 367) comes from a large database of persons with HIV-associated illness. Twenty-three symptoms based on the Sign and Symptom Checklist for Persons with HIV disease and items measuring QOL from the general health status scales were used. Each respondent completed from 1 to 11 questionnaires. The number of reported symptoms had a significant association with patient QOL over time. These findings suggest that appropriate symptom management has the potential to improve patient QOL. This study demonstrates how a state-of-the-art longitudinal modeling technique evaluates the relationship between concurrent rates of change in measurements of two relevant variables.