Regression models for patient-reported measures having ordered categories recorded on multiple occasions

Objectives: The article reviews proportional and partial proportional odds regression for ordered categorical outcomes, such as patient-reported measures, that are frequently used in clinical research in dentistry. Methods: The proportional odds regression model for ordinal data is a generalization of ordinary logistic regression for dichotomous responses. When the proportional odds assumption holds for some but not all of the covariates, the lesser known partial proportional odds model is shown to provide a useful extension. Results: The ordinal data models are illustrated for the analysis of repeated ordinal outcomes to determine whether the burden associated with sensory alteration following a bilateral sagittal split osteotomy procedure differed for those patients who were given opening exercises only following surgery and those who received sensory retraining exercises in conjunction with standard opening exercises. Conclusions: Proportional and partial proportional odds models are broadly applicable to the analysis of cross-sectional and longitudinal ordinal data in dental research.