Book contents
- Applied Longitudinal Data Analysis for Medical Science
- Applied Longitudinal Data Analysis for Medical Science
- Copyright page
- Dedication
- Content
- Preface
- Acknowledgements
- Chapter 1 Introduction
- Chapter 2 Continuous Outcome Variables
- Chapter 3 Continuous Outcome Variables: Regression-based Methods
- Chapter 4 The Modelling of Time
- Chapter 5 Models to Disentangle the Between- and Within-subjects Relationship
- Chapter 6 Causality in Observational Longitudinal Studies
- Chapter 7 Dichotomous Outcome Variables
- Chapter 8 Categorical and Count Outcome Variables
- Chapter 9 Outcome Variables with Floor or Ceiling Effects
- Chapter 10 Analysis of Longitudinal Intervention Studies
- Chapter 11 Missing Data in Longitudinal Studies
- Chapter 12 Sample Size Calculations
- Chapter 13 Software for Longitudinal Data Analysis
- References
- Index
Chapter 7 - Dichotomous Outcome Variables
Published online by Cambridge University Press: 20 April 2023
- Applied Longitudinal Data Analysis for Medical Science
- Applied Longitudinal Data Analysis for Medical Science
- Copyright page
- Dedication
- Content
- Preface
- Acknowledgements
- Chapter 1 Introduction
- Chapter 2 Continuous Outcome Variables
- Chapter 3 Continuous Outcome Variables: Regression-based Methods
- Chapter 4 The Modelling of Time
- Chapter 5 Models to Disentangle the Between- and Within-subjects Relationship
- Chapter 6 Causality in Observational Longitudinal Studies
- Chapter 7 Dichotomous Outcome Variables
- Chapter 8 Categorical and Count Outcome Variables
- Chapter 9 Outcome Variables with Floor or Ceiling Effects
- Chapter 10 Analysis of Longitudinal Intervention Studies
- Chapter 11 Missing Data in Longitudinal Studies
- Chapter 12 Sample Size Calculations
- Chapter 13 Software for Longitudinal Data Analysis
- References
- Index
Summary
In Chapter 7, longitudinal data analysis with a dichotomous outcome variable is discussed. The discussion includes simple methods such as the change in proportions, the McNemar test and Cochrane’s Q as well as regression-based methods such as logistic mixed model analysis and logistic GEE analysis. An important part of this chapter is related to the different results obtained from a logistic GEE analysis and a logistic mixed model analysis. The difference is caused by the fact that GEE analysis is a population average approach, while mixed model analysis is a subject-specific approach. This difference has no influence on the results of a linear mixed model or GEE analysis, but has influence on the results of a logistic mixed model or GEE analysis. It is shown that the results obtained from a logistic GEE analysis are more valid than the results obtained from a logistic mixed model analysis. Also in this chapter, all methods are accompanied by extensive real-life data examples.
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- Applied Longitudinal Data Analysis for Medical ScienceA Practical Guide, pp. 116 - 133Publisher: Cambridge University PressPrint publication year: 2023