A Bayesian decision‐theoretic approach to incorporate preclinical information into phase I oncology trials. Issue 6 (13th April 2020)

• Record Type: Journal Article
• Authors: Zheng, Haiyan; Hampson, Lisa V.
• Is Part Of: Biometrical journal. Volume 62:Issue 6(2020:Oct.)
• Page Start: 1408
• Page End: 1427
• Publication Date: 2020-04-13
• Subjects: Bayesian logistic regression -- decision theory -- historical data -- phase I dose‐finding -- prior‐data conflict; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900161
• Languages: English
• ISSNs: 0323-3847

Abstract

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.