Chapter 3 demonstrates how the mathematics of turning Ordinary Least Squares (OLS) regression inside out can be generalized to Generalized Linear Models (GLM) including logistic, Poisson, negative binomial, random intercept, and fixed effects models.

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.