A beta ‐binomial mixed‐effects model approach for analysing longitudinal discrete and bounded outcomes

AbstractPatient ‐reported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the health‐status of patients and generate data to facilitate improved care. PROs usually appear as discrete an d bounded with U, J, or inverse J shapes, and hence, exponential family members offer inadequate distributional fits. The beta‐binomial distribution has been proposed in the literature to fit PROs. However, the fact that the beta‐binomial distribution does not belong to the exponential family li mits its applicability in the regression model context, and classical estimation approaches are not straightforward. Moreover, PROs are usually measured in a longitudinal framework in which individuals are followed up for a certain period. Hence, each individual obtains several scores of the PRO ove r time, which leads to the repeated measures and defines the correlation structure in the data. In this work, we have developed and proposed an estimation procedure for the analysis of correlated discrete and bounded outcomes, particularly PROs, by a beta‐binomial mixed‐effects model. Additional ly, we have implemented the methodology in thePROreg package in R. Because there are similar approaches in the literature to address the same issue, this work also incorporates a comparison study between our proposal and alternative methodologies commonly implemented in R and sho...
Source: Biometrical Journal - Category: Biotechnology Authors: Tags: RESEARCH PAPER Source Type: research