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Ordinal Regression

In: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling

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  • George J. Knafl

    (University of North Carolina at Chapel Hill, School of Nursing)

Abstract

Ordinal regression modeling of correlated sets of polytomous outcomes using the cumulative logit link function based on either individual outcomes or cumulative outcomes is addressed allowing for non-constant dispersions. For both of these two types of outcomes, formulations are provided for standard generalized estimating equations (GEE) modeling, for partially modified GEE modeling, for fully modified GEE modeling, and for extended linear mixed modeling (ELMM). These formulations include estimating equations, gradient vectors, and Hessian matrices. Alternate correlation structures and their estimation are also addressed.

Suggested Citation

  • George J. Knafl, 2023. "Ordinal Regression," Springer Books, in: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, chapter 0, pages 241-291, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-41988-1_11
    DOI: 10.1007/978-3-031-41988-1_11
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