Regression models for patient-reported measures having ordered categories recorded on multiple occasions
Version of Record online: 11 NOV 2010
© 2010 John Wiley & Sons A/S
Community Dentistry and Oral Epidemiology
Volume 39, Issue 2, pages 154–163, April 2011
How to Cite
Preisser, J. S., Phillips, C., Perin, J. and Schwartz, T. A. (2011), Regression models for patient-reported measures having ordered categories recorded on multiple occasions. Community Dentistry and Oral Epidemiology, 39: 154–163. doi: 10.1111/j.1600-0528.2010.00583.x
- Issue online: 7 MAR 2011
- Version of Record online: 11 NOV 2010
- Submitted 19 February 2010; accepted 21 August 2010
- altered sensation;
- bilateral sagittal split;
- longitudinal ordinal data;
- orthognathic surgery;
- sensory retraining
Preisser JS, Phillips C, Perin J, Schwartz TA. Regression models for patient-reported measures having ordered categories recorded on multiple occasions. Community Dent Oral Epidemiol 2011; 39: 154–163. © 2010 John Wiley & Sons A/S
Abstract – 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.