Folien

This talk presents linear and non-linear mixed effects models for longitudinal data. The analysis of longitudinal data has to deal both with within subject and between subjects variability. Random effects models can accomodate both topics. Estimation of the parameters is frequently done using maximum-likelihood. An outline of ML estimation of the model parameters is given for linear and non-linear mixed effects models.

The talk presents an outline of the use of fixed and mixed effects models using Potthoff's and Roy's orthodontic growth data for the linear mixed effects model. For nonlinear mixed effects models a data set dealing with the concentration time curve of the drug Theophyllin is used. All analyses are performed using the software package R.

References
CE McCulloch and SR Searle Generalized, Linear and Mixed models, Wiley, Chichester, 2001
JC Pinheiro and DM Bates. Mixed-Effects Modesl in S and S-plus. Springer, Berlin, 2000