Applied longitudinal data analysis for epidemiology
In this book, the most important techniques available for longitudinal data analysis are discussed. This includes simple techniques such as the paired t-test, summary statistic, and the (M)ANOVA for repeated measurements, as well as more sophisticated techniques such as generalised estimating equations (GEE) and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation of the results of different techniques. Furthermore, special chapters deal with the analysis of two measurements, experimental studies and the problem of missing data in longitudinal studies. Finally, an extensive overview (and a comparison between) different software packages is provided. This practical guide is suitable for non-statisticians and researchers working with longitudinal data from epidemiological and clinical studies.