Optimizing Trial Designs for Targeted Therapies
Artikel i vetenskaplig tidskrift, 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.

subgroup selection

confirmatory adaptive designs

oncology

predictive biomarker

clinical-trials

subpopulation

bonferroni

population

decision rules

tests

Författare

T. Ondra

Medizinische Universitat Wien

Sebastian Jobjörnsson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

R. A. Beckman

Georgetown University Medical Center

Carl-Fredrik Burman

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

F. Konig

Medizinische Universitat Wien

N. Stallard

Warwick Medical School

M. Posch

Medizinische Universitat Wien

PLoS ONE

1932-6203 (ISSN)

Vol. 11 e0163726

Ämneskategorier

Beroendelära

DOI

10.1371/journal.pone.0163726