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On the use of utility functions for optimizing phase II/phase III seamless trial designs

Authors :
Aouni, Jihane
Bacro, Jean
Toulemonde, Gwladys
Colin, Pierre
Darchy, Loic
Sébastien, Bernard
Institut Montpelliérain Alexander Grothendieck (IMAG)
Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Sanofi Aventis R&D [Chilly-Mazarin]
Université de Montpellier (UM)
Centre National de la Recherche Scientifique (CNRS)
Littoral, Environment: MOdels and Numerics (LEMON)
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG)
Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM)
Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
Littoral, Environnement : Méthodes et Outils Numériques (LEMON)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source :
Journal of clinical trials, Journal of clinical trials, 2021, 10 (415), ⟨10.35248/2167-0870.20.10.415⟩, Journal of clinical trials, longdom publishing, 2021, 10 (415), ⟨10.35248/2167-0870.20.10.415⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

International audience; Background: For several years adaptive designs became more and more popular in the pharmaceutical industry and in particular much attention was brought on adaptive seamless designs. Those designs combine the phase II dose finding trial and the phase III confirmatory trial in a single protocol (with a fixed total sample size). The objective of this paper is to propose some utility-based tools to optimize those designs: first in terms of ratio between phase II and phase III sample sizes, and, second, in patient allocation to doses at the beginning of phase II. Methods: Design optimization methods are generally based either on Fisher information matrix (D-optimality) or on the variance of some statistics of interest (C-optimality). Instead, we propose to define utility functions associated to sponsors' decision related to choice of dose for the phase III and we propose design optimization metrics based on the expected value of this utility. Results and Conclusions: After reviewing and discussing several kinds of utility functions, we focused on two of them, that we have assessed through simulations. We concluded that in most of the scenarios simulated, the expected utility was in a sense more sensitive to the timing of the interim analysis (ratio between phase II over total sample size) than on the patients allocation between the doses. This result points out the fact that it might be necessary to enroll a larger number of patients in phase II to allow an accurate identification of the optimal dose.

Details

Language :
English
ISSN :
21670870
Database :
OpenAIRE
Journal :
Journal of clinical trials, Journal of clinical trials, 2021, 10 (415), ⟨10.35248/2167-0870.20.10.415⟩, Journal of clinical trials, longdom publishing, 2021, 10 (415), ⟨10.35248/2167-0870.20.10.415⟩
Accession number :
edsair.dedup.wf.001..6ddf8c280825ebc5a610efec4891c6c8
Full Text :
https://doi.org/10.35248/2167-0870.20.10.415⟩