Mixed-effects regression modeling of real-time momentary pain assessments in osteoarthritis (OA) patients

Mixed-effects regression models have extraordinary capacity to capture the essence of complex longitudinal data. In this paper we present analyses from a longitudinal study on 157 patients with osteoarthritis (OA) where real-time momentary self-reported pain and physical activity level were collected. We illustrate the application of heterogeneous mixed effect (HME) models to examine the effect of time-varying activity level assessments (sedentary vs. moderate/vigorous) during the 30 min prior to the pain recording on mean pain levels and variability. We also examine the variation in pain associated with activity level and the degree that participant characteristics (weight-bearing vs. non-weight bearing joint w/OA, body mass index, white vs. non-white race, gender) and psychosocial factors (mood, tension, pain catastrophizing) influence pain variation. We fit separate HME models for each characteristic or factor. These HME models are fit with standard statistical software, but there are challenges to fitting and interpreting these models. Once fit the models provide a powerful tool to help guide researchers in understanding highly individualized pain trajectories. © Springer Science+Business Media, LLC 2012.

Duke Scholars

Author Cynthia Jan Coffman Biostatistics & Bioinformatics, Division of Biostatistics

Author Kelli Dominick Allen Medicine, General Internal Medicine