Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -