  We consider the problem of constructing optimal designs for population
pharmacokinetics which use random effect models. It is common practice in the
design of experiments in such studies to assume uncorrelated errors for each
subject. In the present paper a new approach is introduced to determine
efficient designs for nonlinear least squares estimation which addresses the
problem of correlation between observations corresponding to the same subject.
We use asymptotic arguments to derive optimal design densities, and the designs
for finite sample sizes are constructed from the quantiles of the corresponding
optimal distribution function. It is demonstrated that compared to the optimal
exact designs, whose determination is a hard numerical problem, these designs
are very efficient. Alternatively, the designs derived from asymptotic theory
could be used as starting designs for the numerical computation of exact
optimal designs. Several examples of linear and nonlinear models are presented
in order to illustrate the methodology. In particular, it is demonstrated that
naively chosen equally spaced designs may lead to less accurate estimation.
