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Lookup NU author(s): Dr Haiyan ZhengORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley - VCH Verlag GmbH & Co. KGaA, 2020.
For re-use rights please refer to the publisher's terms and conditions.
Leveraging preclinical animal data for a phase I first-in-man trial is appealing yet challenging. A prior based on animal data may place large probability mass on values of the dose-toxicity model parameter(s), which appear infeasible in light of data accrued from the ongoing phase I clinical trial. In this paper, we seek to use animal data to improve decision making in a model-based dose-escalation procedure for phase I oncology trials. Specifically, animal data are incorporated via a robust mixture prior for the parameters of the dose-toxicity relationship. This prior changes dynamically as the trial progresses. After completion of treatment for each cohort, the weight allocated to the informative component, obtained based on animal data alone, is updated using a decision-theoretic approach to assess the commensurability of the animal data with the human toxicity data observed thus far. In particular, we measure commensurability as a function of the utility of optimal prior predictions for the human responses (toxicity or no toxicity) on each administered dose. The proposed methodology is illustrated through several examples and an extensive simulation study. Results show that our proposal can address difficulties in coping with prior-data conflict commencing in sequential trials with a small sample size.
Author(s): Zheng H, Hampson LV
Publication type: Article
Publication status: Published
Journal: Biometrical Journal
Year: 2020
Volume: 62
Issue: 6
Pages: 1408-1427
Print publication date: 02/10/2020
Online publication date: 13/04/2020
Acceptance date: 31/01/2020
Date deposited: 06/02/2020
ISSN (print): 0323-3847
ISSN (electronic): 1521-4036
Publisher: Wiley - VCH Verlag GmbH & Co. KGaA
URL: https://doi.org/10.1002/bimj.201900161
DOI: 10.1002/bimj.201900161
Notes: arXiv preprint arXiv:1907.13620
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