IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-86278-6_5.html
   My bibliography  Save this book chapter

The Analysis of a Dichotomous Outcome Variable

In: Basic Principles of Applied Medical Statistics

Author

Listed:
  • Jos W. R. Twisk

    (Amsterdam UMC, Department of Epidemiology and Data Science)

Abstract

For the analysis of a dichotomous outcome variable cross-tables can be used for the comparison of two groups and the comparison of more than two groups. Based on the cross-table different effect estimates can be derived. Relative risk and risk difference can only be used when the study is prospective, while the odds ratio can be used in all study designs. For statistical testing of these effect estimates, the Fisher exact test or chi-square test can be used. For all these situations, also a regression method is available, i.e. logistic regression analysis. Logistic regression analysis is comparable to linear regression analysis in a way that the regression coefficients have the same interpretation, only the outcome variable is different. In a logistic regression analysis, the outcome variable is the natural log of the odds of having the dichotomous outcome variable. Because of that the regression coefficient for an independent variable can be transformed into an odds ratio. In this chapter, furthermore, the evaluation of linearity of the relationship with a continuous independent variable, the adjustment for confounding and the investigation of effect modification are discussed. The way this is done in logistic regression analysis is exactly the same as in linear regression analysis.

Suggested Citation

  • Jos W. R. Twisk, 2025. "The Analysis of a Dichotomous Outcome Variable," Springer Books, in: Basic Principles of Applied Medical Statistics, chapter 0, pages 87-132, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-86278-6_5
    DOI: 10.1007/978-3-031-86278-6_5
    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

    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:978-3-031-86278-6_5. 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.