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
12 - Software for longitudinal data analysis
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
In the foregoing chapters many research questions have been addressed and many techniques for the analysis of longitudinal data have been discussed. In the examples, the relatively “simple” statistical techniques were performed with SPSS, while the generalized estimating equations (GEE) analyses and the mixed model analyses were performed with Stata. This chapter provides an overview of a few major software packages (i.e. SPSS, SAS, R, and MLwiN) and their ability to perform sophisticated longitudinal data analysis. In this chapter, only GEE analysis and mixed model analysis will be discussed. Multivariate analysis of variance (MANOVA) for repeated measurements can be performed with all major software packages, and can usually be found under the repeated measurements option of the generalized linear model (GLM) or as an extension of the (M)ANOVA procedure. The emphasis of this overview is on the output and syntax of the sophisticated longitudinal analysis in the different software packages, and especially is on the comparison of the results obtained with the various different packages. In this overview, only an-alysis with a continuous and a dichotomous outcome variable will be discussed in detail.
- Type
- Chapter
- Information
- Applied Longitudinal Data Analysis for EpidemiologyA Practical Guide, pp. 243 - 291Publisher: Cambridge University PressPrint publication year: 2013