Optimizing Trial Designs for Targeted Therapies.

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.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: RAB is stockholder in Johnson & Johnson. Furthermore he is Founder and Chief Scientific Officer of Oncomind, LLC. CFB is employee and stake holder of AstraZeneca. MP is head of the Center for Medical Statistics, Informatics, and Intelligent Systems that receives grants from industry. SJ, FK and TO have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.