Latent variable mixture models to address heterogeneity in patient-reported outcome data

Abstract

The populations included in studies that investigate patient-reported outcome (PRO) measures of health or well-being, including health-related quality of life, are often heterogeneous with respect to their sociodemographic and health status characteristics. If the sources of heterogeneity are not observed or are not known a priori, latent variable mixture models (LVMMs) can be used to identify homogeneous sub-groups within the study population based on observed patterns of responses in PRO data. Our purpose is to review the characteristics of LVMMs and their applications for PRO data, and provide a demonstration of their use. We focus on mixture item response theory (IRT) models, which combine latent class analysis with the conventional IRT model to define the measurement model for one or more latent variables. In PRO studies, IRT models can be used to assess differential item functioning and response shift. An illustrative example is presented using clinical registry data for 1391 total hip replacement patients who provided responses for the physical component items of the 12-item Short Form Health Survey (SF-12). After assessing model fit and class discrimination statistics, a three-class model was selected. Model parameter estimates across classes were dissimilar for many of the items. Sex and self-reported presence of arthritis and back pain were associated with class membership. LVMMs represent a potentially useful tool to explore patterns of responses in PRO data. Opportunities for other applications of LVMMs to PRO data are discussed.

Keywords: Differential item functioning; Item response theory; Latent class analysis; Mixture; Self-report measures.