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Optimizing Trial Designs for Targeted Therapies

Journal article
Authors T. Ondra
Sebastian Jobjörnsson
R. A. Beckman
Carl-Fredrik Burman
F. Konig
N. Stallard
M. Posch
Published in Plos One
Volume 11
Issue 9
ISSN 1932-6203
Publication year 2016
Published at Department of Mathematical Sciences
Department of Mathematical Sciences, Applied Mathematics and Statistics
Language en
Links dx.doi.org/10.1371/journal.pone.016...
Keywords confirmatory adaptive designs, clinical-trials, predictive biomarker, subgroup selection, decision rules, oncology, subpopulation, population, bonferroni, tests
Subject categories Substance Abuse

Abstract

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.

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