In Chapter 3, regression-based methods to analyse longitudinal data are introduced. Linear mixed models analysis and linear GEE mixed model analysis are explained in detail, while the adjustment for covariance method is explained in less detail. It is shown that the different regression-based methods adjust for the correlated observations within the subject in a different way; linear mixed model analysis by allowing different regression coefficients for different subjects (i.e. random intercept and random slope(s)), GEE analysis by estimating directly the correlation between the repeated observations within the subject by assuming a priori a certain correlation structure. It is explained that a linear mixed model analysis with only a random intercept is basically the same as a linear GEE analysis with an exchangeable correlation structure. In this chapter, special attention is given to the interpretation of the regression coefficient, which is a weighted average of the between-subjects relationship and the within-subjects relationship. All methods are accompanied by extensive real-life data examples.

In Chapter 11 the problem of missing data is discussed. Missing data always occurs in longitudinal studies and can be divided based on the missing data mechanism: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). The problem of the distinction in missing data mechanisms is that it is highly theoretical. More important is the distinction between informative and non-informative missing data. An important part of this chapter deals with imputation methods, such as last value carries forward and multiple imputation. An important conclusion of example studies shown in this chapter is that multiple imputation is, in general, not necessary for missing data in longitudinal studies. It is even better not to impute the missing data and us mixed model analysis for the longitudinal data analysis. In this chapter it is also shown that mixed model analysis deals slightly better with missing data than GEE analysis, although the differences between the two methods are not as great as often suggested.