A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data

TL;DR

This paper introduces a Bayesian hierarchical model that effectively integrates animal data into phase I clinical trials, improving decision-making across heterogeneous patient subgroups by leveraging shared information and accounting for potential differences.

Contribution

The paper presents a novel hierarchical modeling approach that bridges animal and human data in phase I trials, allowing for flexible exchangeability assumptions and robustness to data inconsistencies.

Findings

Model provides robust inferences when data sources are inconsistent.

Simulation shows improved decision accuracy over existing methods.

Approach effectively handles heterogeneity across patient subgroups.

Abstract

Incorporating preclinical animal data, which can be regarded as a special kind of historical data, into phase I clinical trials can improve decision making when very little about human toxicity is known. In this paper, we develop a robust hierarchical modelling approach to leverage animal data into new phase I clinical trials, where we bridge across non-overlapping, potentially heterogeneous patient subgroups. Translation parameters are used to bring both historical and contemporary data onto a common dosing scale. This leads to feasible exchangeability assumptions that the parameter vectors, which underpin the dose-toxicity relationship per study, are assumed to be drawn from a common distribution. Moreover, human dose-toxicity parameter vectors are assumed to be exchangeable either with the standardised, animal study-specific parameter vectors, or between themselves. Possibility of non-exchangeability for each parameter vector is considered to avoid inferences for extreme subgroups being overly influenced by the other. We illustrate the proposed approach with several trial data examples, and evaluate the operating characteristics of our model compared with several alternatives in a simulation study. Numerical results show that our approach yields robust inferences in circumstances, where data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.