Xiaoming Gao1, Todd A Schwartz2, John S Preisser3, Jamie Perin4. 1. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, United States of America. Electronic address: xgao@fhi360.org. 2. Department of Biostatistics, Gillings School of Global Public Health & School of Nursing, University of North Carolina at Chapel Hill, 3106E McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599-7420 United States of America. Electronic address: Todd_Schwartz@unc.edu. 3. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 3105F McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599-7420, United States of America. 4. Department of International Health, Johns Hopkins University, Baltimore, MD 21205, United States of America.
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.
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.
Authors: Karen Nieves-Lugo; Deanna Ware; M Reuel Friedman; Sabina Haberlen; James Egan; Andre L Brown; Omar Dakwar; Michael Plankey Journal: AIDS Care Date: 2019-09-23
Authors: Henning Andersen; Renato Mantegazza; Jing Jing Wang; Fanny O'Brien; Kaushik Patra; James F Howard Journal: Qual Life Res Date: 2019-03-23 Impact factor: 4.147