Abstract

Nonlinear mixed effects models are frequently used in the analysis of grouped data. Specially in pharmacological studies the observed individuals usually share a common response structure, such that information from individual responses might be merged to obtain efficient estimates. The mixed effects Models can be used to model population studies by assuming the individual parameter vectors to be realizations of independently distributed random variables, what yields for nonlinear response functions of the individual parameters nontrivial models. Unfortunately, in nonlinear mixed effects models problems occur, as there exists no closed form representation of the likelihood-function of the observations and hence no closed form of the Fisher Information. Optimal designs in nonlinear mixed effects models are usually based on approximations of the Fisher Information, such that bad approximations might lead to bad experimental designs. In this talk we discuss different approaches for approximating the information matrix and the influence of the approximations on the implied designs in pharmacokinetic studies.