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

J S Preisser1, C Phillips, J Perin

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. jpreisse@bios.unc.edu

Community Dentistry and Oral Epidemiology
|November 13, 2010
PubMed
Summary

This study reviews proportional and partial proportional odds regression models for analyzing ordered categorical data in dental research. These statistical methods are valuable for understanding patient-reported outcomes in clinical dentistry.

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Area of Science:

  • Statistics
  • Biostatistics
  • Dental Research

Background:

  • Ordered categorical outcomes are common in clinical dentistry, particularly patient-reported measures.
  • Traditional logistic regression is limited for such data.
  • Proportional odds regression offers a generalization for ordinal responses.

Purpose of the Study:

  • To review proportional and partial proportional odds regression models.
  • To highlight their application in analyzing ordered categorical outcomes in dental research.
  • To demonstrate their utility for patient-reported measures.

Main Methods:

  • The proportional odds (PO) regression model is a generalization of logistic regression for ordinal data.
  • The partial proportional odds (PPO) model extends PO regression when the proportional odds assumption is violated for some covariates.
  • Both models are suitable for analyzing cross-sectional and longitudinal ordinal data.

Main Results:

  • The study illustrates the application of PO and PPO models using repeated ordinal outcomes.
  • The analysis determined differences in sensory alteration burden after bilateral sagittal split osteotomy based on post-surgical exercise regimens.
  • This demonstrates the practical utility of these models in clinical dental scenarios.

Conclusions:

  • Proportional odds and partial proportional odds models are effective for analyzing ordinal data in dentistry.
  • These models are broadly applicable to both cross-sectional and longitudinal study designs.
  • They provide robust methods for evaluating patient-reported outcomes in dental research.