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
Preface
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
The most important feature of this book is the word “applied” in the title. This implies that the emphasis of this book lies more on the application of statistical techniques for longitudinal data analysis and not so much on the mathematical background. In most other books on the topic of longitudinal data analysis, the mathematical background is the major issue, which may not be surprising since (nearly) all the books on this topic have been written by statisticians. Although statisticians fully understand the difficult mathematical material underlying longitudinal data analysis, they often have difficulty in explaining this complex material in a way that is understandable for the researchers who have to use the technique or interpret the results. Therefore, this book is not written by a statistician, but by an epidemiologist. In fact, an epidemiologist is not primarily interested in the basic (difficult) mathematical background of the statistical methods, but in finding the answer to a specific research question; the epidemiologist wants to know how to apply a statistical technique and how to interpret the results. Owing to their different basic interests and different level of thinking, communication problems between statisticians and epidemiologists are quite common. This, in addition to the growing interest in longitudinal studies, initiated the writing of this book: a book on longitudinal data analysis, which is especially suitable for the “non-statistical” researcher (e.g. the epidemiologist). The aim of this book is to provide a practical guide on how to handle epidemiological data from longitudinal studies. The purpose of this book is to build a bridge over the communication gap that exists between statisticians and epidemiologists when addressing the complicated topic of longitudinal data analysis.
- Type
- Chapter
- Information
- Applied Longitudinal Data Analysis for EpidemiologyA Practical Guide, pp. xiiiPublisher: Cambridge University PressPrint publication year: 2013