GEEORD: A SAS macro for analyzing ordinal response variables with repeated measures through proportional odds, partial proportional odds, or non-proportional odds models

Xiaoming Gao, Todd A. Schwartz, John S. Preisser, Jamie L. Perin

Research output: Contribution to journalArticlepeer-review

Abstract

Background and objective A SAS macro, GEEORD, has been developed for the analysis of ordinal responses with repeated measures through a regression model that flexibly allows the proportional odds assumption to apply (or not) separately for each explanatory variable. Methods and results Previously utilized in an analysis of a longitudinal orthognathic surgery clinical trial by Preisser et al. [1,2], the basis of GEEORD is the generalized estimating equations (GEE) method for cumulative logits models described by Lipsitz et al. [3]. The macro extends the capabilities for modeling correlated ordinal data of GEECAT, a SAS macro that allows the user to model correlated categorical response data [4]. The macro applies to independent ordinal responses as a special case. Applications and conclusions Examples are provided to demonstrate the convenient application of GEEORD to two different datasets. The macro's features are illustrated in fitting models to ordinal response variables in univariate and repeated measures settings; this includes the capacity to fit the non-proportional odds model, the partial proportional odds model, and the proportional odds model. The macro additionally provides relevant tests of the proportional odds assumption.

Original languageEnglish (US)
Pages (from-to)23-30
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume150
DOIs
StatePublished - Oct 1 2017

Keywords

  • Cumulative logits
  • Generalized estimating equations
  • Ordinal responses
  • Partial proportional odds
  • Proportional odds
  • Repeated measures design

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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