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 2 - Continuous 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 2, simple methods to analyse longitudinal data are introduced. First, methods for analysing the change over time for longitudinal studies with two repeated measurements. Second, methods for analysing the change over time for longitudinal studies with more than two repeated measurements. And finally, methods for analysing the difference in the change over time between groups for longitudinal studies with both two and more than two repeated measurements. Although both the parametric and non-parametric versions of these methods are discussed, a very detailed explanation of GLM for repeated measures is provided. All methods are accompanied by extensive real-life data examples.
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- Applied Longitudinal Data Analysis for Medical ScienceA Practical Guide, pp. 7 - 30Publisher: Cambridge University PressPrint publication year: 2023