The article reviews univariate and longitudinal proportional and partial proportional odds regression for ordered categorical outcomes, such as patient reported measures, that are frequently used in biomedical and dental research. These regression models consist of the logit link function applied to sets of cumulative odds. When the proportional odds assumption applies to some but not all of the covariates, the partial proportional odds model may be used. The ordinal data models are illustrated for the analysis of repeated ordinal outcomes to determine if the burden associated with sensory alteration following jaw surgery 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. Proportional and partial proportional odds models are straightforward to apply in analyses of cross-sectional and longitudinal ordinal data.


Longitudinal Data Analysis and Time Series | Statistical Models