Multidimensional beta-binomial regression model: A joint analysis of patient-reported outcomes

Patient-reported outcomes (PROs) are often used as primary outcomes in clinical research studies. PROs are usually measured in ordinal scales and they tend to have excess variability beyond the binomial distribution, a property called overdispersion. Beta-binomial distribution has been previously proposed in this context in order to fit PROs, and beta-binomial regression (BBR) as a good alternative for modelling purposes, including the extension to mixed-effects models in a longitudinal framework. Many PROs have various health dimensions, which are commonly correlated within subjects. However, in clinical analysis, dimensions are separately analysed. In this work, we propose a multidimensional BBR model that incorporates a multidimensional outcome including several PROs in a joint analysis. The proposal has been evaluated and compared to the independent analysis through a simulation study and a real data application with patients with respiratory disease. Results show the advantages that a multidimensional approach offers in terms of parameter significance and interpretation. Additionally, the methods proposed in this work are implemented in the PROreg R-package developed by the authors.