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

## TL;DR

This paper introduces a Bayesian decision-theoretic method that adaptively incorporates preclinical animal data into phase I oncology trials, improving dose-escalation decisions by dynamically updating priors based on data compatibility.

## Contribution

It presents a novel mixture prior approach that adjusts as the trial progresses, effectively handling prior-data conflicts in small-sample, sequential phase I trials.

## Key findings

- Improves dose-escalation decision accuracy.
- Effectively manages prior-data conflicts.
- Enhances trial safety and efficiency.

## Abstract

Leveraging preclinical animal data for a phase I first-in-man trial is appealing yet challenging. A prior based on animal data may place large probability mass on values of the dose-toxicity model parameter(s), which appear infeasible in light of data accrued from the ongoing phase I clinical trial. In this paper, we seek to use animal data to improve decision making in a model-based dose-escalation procedure for phase I oncology trials. Specifically, animal data are incorporated via a robust mixture prior for the parameters of the dose-toxicity relationship. This prior changes dynamically as the trial progresses. After completion of treatment for each cohort, the weight allocated to the informative component, obtained based on animal data alone, is updated using a decision-theoretic approach to assess the commensurability of the animal data with the human toxicity data observed thus far. In particular, we measure commensurability as a function of the utility of optimal prior predictions for the human responses (toxicity or no toxicity) on each administered dose. The proposed methodology is illustrated through several examples and an extensive simulation study. Results show that our proposal can address difficulties in coping with prior-data conflict commencing in sequential trials with a small sample size.