  An important objective in the development of targeted therapies is to
identify the populations where the treatment under consideration has positive
benefit risk balance. We consider pivotal clinical trials, where the efficacy
of a treatment is tested in an overall population and/or in a pre-specified
subpopulation. Based on a decision theoretic framework we derive optimized
trial designs by maximizing utility functions. Features to be optimized include
the sample size and the population in which the trial is performed (the full
population or the targeted subgroup only) as well as the underlying multiple
test procedure. The approach accounts for prior knowledge of the efficacy of
the drug in the considered populations using a two dimensional prior
distribution. The considered utility functions account for the costs of the
clinical trial as well as the expected benefit when demonstrating efficacy in
the different subpopulations. We model utility functions from a sponsor's as
well as from a public health perspective, reflecting actual civil interests.
Examples of optimized trial designs obtained by numerical optimization are
presented for both perspectives.
