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

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A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials. / Zheng, H.; Hampson, L.V.

In: Biometrical Journal, Vol. 62, No. 6, 01.10.2020, p. 1408-1427.

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Zheng H, Hampson LV. A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials. Biometrical Journal. 2020 Oct 1;62(6):1408-1427. Epub 2020 Apr 13. doi: 10.1002/bimj.201900161

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@article{01ba1dc819e744d0a7e79a2bf4c40c7e,
title = "A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials",
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.",
keywords = "Bayesian logistic regression, decision theory, historical data, phase I dose-finding, prior-data conflict",
author = "H. Zheng and L.V. Hampson",
year = "2020",
month = oct,
day = "1",
doi = "10.1002/bimj.201900161",
language = "English",
volume = "62",
pages = "1408--1427",
journal = "Biometrical Journal",
issn = "0323-3847",
publisher = "Wiley-VCH Verlag",
number = "6",

}

RIS

TY - JOUR

T1 - A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials

AU - Zheng, H.

AU - Hampson, L.V.

PY - 2020/10/1

Y1 - 2020/10/1

N2 - 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.

AB - 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.

KW - Bayesian logistic regression

KW - decision theory

KW - historical data

KW - phase I dose-finding

KW - prior-data conflict

U2 - 10.1002/bimj.201900161

DO - 10.1002/bimj.201900161

M3 - Journal article

VL - 62

SP - 1408

EP - 1427

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 6

ER -