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
1 - Introduction
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
Longitudinal studies are defined as studies in which the outcome variable is repeatedly measured; i.e. the outcome variable is measured in the same subject on several occasions. In longitudinal studies the observations of one subject over time are not independent of each other, and therefore it is necessary to apply special statistical techniques, which take into account the fact that the repeated observations of each subject are correlated. The definition of longitudinal studies (used in this book) implicates that statistical techniques like survival analyses are beyond the scope of this book. Those techniques basically are not longitudinal data analysing techniques because (in general) the outcome variable is an irreversible endpoint and therefore strictly speaking is only measured at one occasion. After the occurrence of an event no more observations are carried out on that particular subject.
Why are longitudinal studies so popular these days? One of the reasons for this popularity is that there is a general belief that with longitudinal studies the problem of causality can be solved. This is, however, a typical misunderstanding and is only partly true. Table 1.1 shows the most important criteria for causality, which can be found in every epidemiological textbook (e.g. Rothman and Greenland, 1998). Only one of them is specific for a longitudinal study: the rule of temporality. There has to be a time-lag between outcome variable Y (effect) and covariate X (cause); in time the cause has to precede the effect. The question of whether or not causality exists can only be (partly) answered in specific longitudinal studies (i.e. experimental studies) and certainly not in all longitudinal studies. What then is the advantage of performing a longitudinal study? A longitudinal study is expensive, time consuming, and difficult to analyze. If there are no advantages over cross-sectional studies why bother? The main advantage of a longitudinal study compared to a cross-sectional study is that the individual development of a certain outcome variable over time can be studied.
- Type
- Chapter
- Information
- Applied Longitudinal Data Analysis for EpidemiologyA Practical Guide, pp. 1 - 5Publisher: Cambridge University PressPrint publication year: 2013