Optimized adaptive enrichment designs

Journal article, 2019

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for
the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated.
In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of
different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor,
maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new
treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment,
where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a
dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which
are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying
population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore,
adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the
expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that
trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal
view and may include the overall population (with patients from the complement of the subgroup) even if there is
substantial evidence that the therapy is only effective in the subgroup.