In Chapter 7, longitudinal data analysis with a dichotomous outcome variable is discussed. The discussion includes simple methods such as the change in proportions, the McNemar test and Cochrane’s Q as well as regression-based methods such as logistic mixed model analysis and logistic GEE analysis. An important part of this chapter is related to the different results obtained from a logistic GEE analysis and a logistic mixed model analysis. The difference is caused by the fact that GEE analysis is a population average approach, while mixed model analysis is a subject-specific approach. This difference has no influence on the results of a linear mixed model or GEE analysis, but has influence on the results of a logistic mixed model or GEE analysis. It is shown that the results obtained from a logistic GEE analysis are more valid than the results obtained from a logistic mixed model analysis. Also in this chapter, all methods are accompanied by extensive real-life data examples.

In Chapter 13, a few examples which were evaluated in the preceding chapters of this book are reanalysed with different software programmes such as SAS, R and SPSS. For all examples, both output and syntax is provided. The most important conclusion of all the analyses is that mixed model analysis and GEE analysis with a continuous outcome variable are very stable and lead to the same results, independent of the software programme used. This also holds for logistic GEE analysis, but is totally different for logistic mixed model analysis. The results of the logistic mixed model analysis are highly dependent on the estimation procedure used. The most important estimation procedures used are the Gauss-Hermite quadrature method and the residual pseudo-likelihood method. It is argued that the Gauss-Hermite estimation procedure provides the most valid results of a logistic mixed model analysis.