IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-41988-1_13.html
   My bibliography  Save this book chapter

Example Multinomial and Ordinal Regression Analyses

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

Author

Listed:
  • George J. Knafl

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

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.

Suggested Citation

  • George J. Knafl, 2023. "Example Multinomial and Ordinal Regression Analyses," Springer Books, in: Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling, chapter 0, pages 351-384, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-41988-1_13
    DOI: 10.1007/978-3-031-41988-1_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-41988-1_13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.