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Optimizing Trial Designs for Targeted Therapies

T. Ondra ; Sebastian Jobjörnsson (Institutionen för matematiska vetenskaper, Tillämpad matematik och statistik) ; R. A. Beckman ; Carl-Fredrik Burman (Institutionen för matematiska vetenskaper) ; F. Konig ; N. Stallard ; M. Posch
PLoS ONE (1932-6203). Vol. 11 (2016), 9,
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: confirmatory adaptive designs, clinical-trials, predictive biomarker, subgroup selection, decision rules, oncology, subpopulation, population, bonferroni, tests



Denna post skapades 2016-11-16. Senast ändrad 2016-12-20.
CPL Pubid: 245271

 

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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, Tillämpad matematik och statistikInstitutionen för matematiska vetenskaper, Tillämpad matematik och statistik (GU)
Institutionen för matematiska vetenskaperInstitutionen för matematiska vetenskaper (GU)

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