The notion of one treatment serving a large homogeneous patient
population is becoming increasingly hard to sustain. Many recent
studies are designed to understand and address heterogeneity of
patient populations, exploiting features of adaptive treatment
allocations, population enrichment and sequential stopping. In an
increasing number of studies the discovery of relevant subpopulations
for such adaptive treatment is part of the trial design.
We review some novel clinical trial designs that implement such
schemes, using examples with increasing levels of adaptation. First,
we start the discussion with adaptation based on a patient's first
cycle response in a two-cycle treatment. Next we continue with
dynamic treatment regimens that include adaptation on the outcome from
the initial front-line therapy. The discussion includes an adjustment
for lack of randomization in the assignment of later stage salvage
therapies. Third, we review a basket trial design for a study of
targeted therapies for cancer. In this study adaptation includes the
selection of disease, treatment and a patient subpopulation. Common
to these examples is the notion of quantifying the value of
alternative treatment allocations and outcomes. In all examples we do
this using a utility function that formalizes, for example, the
tradeoff of toxicity and efficacy outcomes. A last example shows
another application of such utility-based designs. This time without
the context of adaptation.