  Hierarchical random effect models are used for different purposes in clinical
research and other areas. In general, the main focus is on population
parameters related to the expected treatment effects or group differences among
all units of an upper level (e.g. subjects in many settings). Optimal design
for estimation of population parameters are well established for many models.
However, optimal designs for the prediction for the individual units may be
different. Several settings are identiffed in which individual prediction may
be of interest. In this paper we determine optimal designs for the individual
predictions, e.g. in multi-center trials, and compare them to a conventional
balanced design with respect to treatment allocation. Our investigations show,
that balanced designs are far from optimal if the treatment effects vary
strongly as compared to the residual error and more subjects should be
recruited to the active (new) treatment in multi-center trials. Nevertheless,
effciency loss may be limited resulting in a moderate sample size increase when
individual predictions are foreseen with a balanced allocation.
