Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgements
- 1 Introduction
- 2 Study design
- 3 Continuous outcome variables
- 4 Continuous outcome variables – relationships with other variables
- 5 The modeling of time
- 6 Other possibilities for modeling longitudinal data
- 7 Dichotomous outcome variables
- 8 Categorical and “count” outcome variables
- 9 Analysis of experimental studies
- 10 Missing data in longitudinal studies
- 11 Sample size calculations
- 12 Software for longitudinal data analysis
- 13 One step further
- References
- Index
9 - Analysis of experimental studies
Published online by Cambridge University Press: 05 May 2013
- Frontmatter
- Contents
- Preface
- Acknowledgements
- 1 Introduction
- 2 Study design
- 3 Continuous outcome variables
- 4 Continuous outcome variables – relationships with other variables
- 5 The modeling of time
- 6 Other possibilities for modeling longitudinal data
- 7 Dichotomous outcome variables
- 8 Categorical and “count” outcome variables
- 9 Analysis of experimental studies
- 10 Missing data in longitudinal studies
- 11 Sample size calculations
- 12 Software for longitudinal data analysis
- 13 One step further
- References
- Index
Summary
Introduction
Experimental (longitudinal) studies differ from observational longitudinal studies in that experimental studies (in epidemiology often described as trials) include one or more interventions. In general, before the intervention (i.e. at baseline) the population is (randomly) divided into two or more groups. In the case of two groups, one of the groups receives the intervention of interest and the other group receives a placebo intervention, no intervention at all, or the “usual” treatment. The latter is known as the control group. Both groups are monitored over a certain period of time, in order to find out whether the groups differ with regard to a particular outcome variable. The outcome variable can be continuous, dichotomous, or categorical.
In epidemiology, the simplest form of an experimental longitudinal study is one in which a baseline measurement and only one follow-up measurement are performed (Figure 9.1). If the subjects are randomly assigned to the different groups (interventions), a comparison of the follow-up values between the groups will give an answer to the question of which intervention is more effective with regard to the particular outcome variable. The assumption is that random allocation at baseline will ensure that there is no difference between the groups at baseline (in fact, in this situation a baseline measure is not even necessary).
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- Information
- Applied Longitudinal Data Analysis for EpidemiologyA Practical Guide, pp. 163 - 211Publisher: Cambridge University PressPrint publication year: 2013
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