Example Multinomial and Ordinal Regression Analyses

George J. Knafl ()
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George J. Knafl: University of North Carolina at Chapel Hill, School of Nursing

Chapter Chapter 13 in Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, 2026, pp 421-454 from Springer

Abstract: Abstract Adaptive analyses are presented of a trichotomous respiratory status data set with an outcome having three possible values over a baseline and four subsequent clinic visits using multinomial regression with the generalized logit link function and ordinal regression with the cumulative logit link function based on either individual or cumulative outcomes. Results are compared for partially modified generalized estimating equations (GEE), fully modified GEE, and extended linear mixed modeling (ELMM). Linearity of the generalized and cumulative logits of the means in visit with constant dispersions is addressed as well as whether unit dispersions are appropriate for these data, a comparison to standard GEE, and the dependence of means and dispersions on visit. Adaptive additive and adaptive moderation models are generated for visit and being on active treatment. A summary of the analysis results is also provided. SAS code for generating these analyses is described along with output generated by that code.

Keywords: Direct variance modeling; Extended linear mixed modeling; Generalized estimating equations; Moderation; Multinomial regression; Ordinal regression (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00989-0_13

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DOI: 10.1007/978-3-032-00989-0_13

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