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 5 - Models to Disentangle the Between- and Within-subjects Relationship
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
One of the most complicated parts of a longitudinal data analysis is the interpretation of the regression coefficient. The regression coefficient is a weighted average of the between-subjects relationship and the within-subjects relationship. In Chapter 5, first hybrid models are introduced. Hybrid models are developed to disentangle the between- and within-subjects relationship. Hybrid models can be performed by calculating: (1) the individual average value of the covariate, which is used to obtain the between-subjects part of the relationship and (2) the deviation score, which is the difference between the observed values and the individual mean value and is used to obtain the within-subjects part of the relationship. In this chapter the modelling of changes and the autoregressive model are also discussed. Both models intend to estimate only the within-subjects part of the longitudinal relationship. All methods are accompanied by extensive real-life data examples.
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- Applied Longitudinal Data Analysis for Medical ScienceA Practical Guide, pp. 76 - 91Publisher: Cambridge University PressPrint publication year: 2023