Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • Chapter 3 - Continuous Outcome Variables: Regression-based Methods

  • Jos W. R. Twisk, Amsterdam University Medical Centers
  • Book:
    Applied Longitudinal Data Analysis for Medical Science
    Published online:
    20 April 2023
    Print publication:
    27 April 2023, pp 31-55

  • Summary

    In Chapter 3, regression-based methods to analyse longitudinal data are introduced. Linear mixed models analysis and linear GEE mixed model analysis are explained in detail, while the adjustment for covariance method is explained in less detail. It is shown that the different regression-based methods adjust for the correlated observations within the subject in a different way; linear mixed model analysis by allowing different regression coefficients for different subjects (i.e. random intercept and random slope(s)), GEE analysis by estimating directly the correlation between the repeated observations within the subject by assuming a priori a certain correlation structure. It is explained that a linear mixed model analysis with only a random intercept is basically the same as a linear GEE analysis with an exchangeable correlation structure. In this chapter, special attention is given to the interpretation of the regression coefficient, which is a weighted average of the between-subjects relationship and the within-subjects relationship. All methods are accompanied by extensive real-life data examples.