  With the help of Generalized Estimating Equations, we identify locally
D-optimal crossover designs for generalized linear models. We adopt the
variance of parameters of interest as the objective function, which is
minimized using constrained optimization to obtain optimal crossover designs.
In this case, the traditional general equivalence theorem could not be used
directly to check the optimality of obtained designs. In this manuscript, we
derive a corresponding general equivalence theorem for crossover designs under
generalized linear models.
