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.

中文翻译：

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将临床前信息纳入 I 期肿瘤学试验的贝叶斯决策理论方法

利用临床前动物数据进行 I 期肿瘤学试验既有吸引力又具有挑战性。在本文中，我们使用动物数据来改进基于模型的剂量递增程序中的决策。我们就如何在小样本量的顺序研究中测量和解决先验数据冲突提出了建议。动物数据通过稳健的双组分混合物先于人类剂量-毒性关系的参数合并。放置在先验每个组件上的权重是根据经验选择的，并随着试验的进行和更多数据的积累而动态更新。在每个队列完成后，我们使用贝叶斯决策理论方法来评估动物数据对观察到的人类毒性结果的预测效用，反映动物和人类剂量毒性关系之间的一致性程度。