Optimizing Trial Designs for Targeted Therapies
Journal article, 2016

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.

confirmatory adaptive designs

subgroup selection



predictive biomarker



decision rules




T. Ondra

Medical University of Vienna

Sebastian Jobjörnsson

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

R. A. Beckman

Georgetown University

Carl-Fredrik Burman

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

F. Konig

Medical University of Vienna

N. Stallard

The University of Warwick

M. Posch

Medical University of Vienna


1932-6203 (ISSN) 19326203 (eISSN)

Vol. 11 9 e0163726

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