Crossover designs are designs of experiments in order to compare
effects of different treatments by applying them to a number of subjects
during a sequence of periods. That means each subject will have repeated
measurements based on different treatments. Subject dropout is very common
in practical applications of crossover designs. However, there is very
limited literature of experimental design taking this into account.
Optimality results have not yet been well established due to complexity of
the problem. This paper establishes feasible necessary and sufficient
conditions for a crossover design to be universally optimal in approximate
design theory under the presence of subject dropout. These conditions are
essentially linear equations with respect to proportions of all possible
treatment sequences being applied to subjects and hence they can be easily
solved. A general algorithm is proposed to derive exact designs which are
shown to be efficient and robust.