A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials

Zheng, H. and Hampson, L.V. (2020) A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials. Biometrical Journal. ISSN 0323-3847

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Abstract

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision-making in a model-based dose-escalation procedure. We make a proposal for how to measure and address a prior-data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two-component mixture prior for the parameters of the human dose-toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision-theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose-toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study.

Item Type:
Journal Article
Journal or Publication Title:
Biometrical Journal
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700
Subjects:
ID Code:
143704
Deposited By:
Deposited On:
17 Jul 2020 13:05
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Jul 2020 14:40