Abstract
ABSTRACT
The goal of dose‐finding oncology trials is to assess the safety of anti‐cancer treatments across multiple doses and to recommend dose(s) for subsequent trials. As patients' outcomes accrue, trialists dynamically recommend new doses for further investigation during the trial. This adaptive decision‐making lends itself to Bayesian learning, with Bayesian frameworks increasingly guiding dose recommendations in model‐based dose‐finding designs, including the Continual Reassessment Method (CRM). However, such approaches introduce increased complexity, not least when additional outcomes are incorporated within designs. Such trial designs require careful prior selection. Directly applying prior calibration methods developed for single outcome model‐based trial designs to joint outcome trial designs may introduce unintended bias in dose recommendations, potentially limiting dose exploration and failing to accurately reflect trialists' a priori beliefs. We extend methodology to analytically calibrate priors for model‐based joint outcome trial designs with divergence minimisation. Our method offers an analytical and computationally efficient technique. We demonstrate the flexibility of our calibration method relative to existing approaches in ensemble simulation scenarios, and show that calibrating priors in this way delivers improved accuracy and computational efficiency compared with traditional grid search methods. As Bayesian dose‐finding trial designs continue to advance, research and guidance on the effective calibration of design parameters is essential to support uptake and ensure optimal performance in practice. This method provides an analytical and intuitive approach to prior calibration, highlighting the importance of rigorous prior calibration in improving model accuracy and dose selection for safer, more effective oncology treatments.