Optimal design of experiments for hypothesis testing on ordered treatments via intersection-union tests

We find experimental plans for hypothesis testing when a prior ordering of experimental groups or treatments is expected. Despite the practical interest of the topic, namely in dose finding, algorithms for systematically calculating good plans are still elusive. Here, we consider the intersection-union principle for constructing optimal experimental designs for testing hypotheses about ordered treatments. We propose an optimization-based formulation to handle the problem when the power of the test is to be maximized. This formulation yields a complex objective function which we handle with a surrogate-based optimizer. The algorithm proposed is demonstrated for several ordering relations. The relationship between designs maximizing power for the intersection-union test (IUT) and optimality criteria used for linear regression models is analyzed; we demonstrate that IUT-based designs are well approximated by C-optimal designs and maximum entropy sampling designs while D $$_{\text {A}}$$ A -optimal designs are equivalent to balanced designs. Theoretical and numerical results supporting these relations are presented.