Location-Scale Latent Process Model for Repeated Ordinal Patient-Reported Outcomes

Patient-reported outcomes (PROs) are collected daily in clinical trials to measure patients' quality of life, for example by capturing symptoms. These data are often reported on a small-range ordinal scale and analyzed without consideration of their longitudinal characteristics. The emergence of electronic data collection methods for home-based measurements has enabled routine, daily capture of various symptom scores, highlighting the need for statistical methods to analyze frequent ordinal longitudinal data. Both their mean structure over time and variability, which are known to be linked to disease progression, are of interest and can be affected by treatment. To model the dynamics of ordinal PROs, we propose a location-scale latent process model that includes two types of variability across patients: individual underlying level flexibly modeled over time (e.g., with splines) using random effects and covariates, and individual short-term variability with the error variance expressed as a linear structure of covariates (e.g., treatment) and a patient-specific random intercept. The model is estimated in a maximum likelihood framework with an interface in R. The multidimensional intractable integrals in the optimization are approximated using a Quasi-Monte Carlo method. The estimation procedure is validated by a simulation study and we apply the methodology to data from two clinical trials, one in asthma and one in chronic obstructive pulomonary disease (COPD), to evaluate the effect of treatment on the dynamics of various respiratory symptoms and their variability.