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arXiv · 2608.13423

COBRA-DOSE: Copula-based Bayesian Model Averaging for Dose Selection

Luke Hagar·Min Zhang·Ranjeny Thomas·Andrew J. Martin
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Abstract

Early-phase clinical trials for dose selection typically enrol few patients and aim to identify doses that are both safe and promising for further study. While traditional approaches identify the maximum tolerated dose, modern trials for targeted therapies often seek the optimal biological dose, defined as the lowest dose achieving sufficient biological activity with acceptable safety. In immunology settings, assessment of biological activity is based on multiple biomarkers or clinical endpoints. Clinicians leading dose-selection efforts would thus benefit from transparent summaries of the probabilities of observing combinations of biomarker outcomes across doses. However, such inference is challenging in small samples where complex modelling assumptions are difficult to verify. To address this limitation, we propose COBRA-DOSE, a framework for posterior predictive inference based on two endpoints that models dependence via copulas and accounts for uncertainty in both marginal distributions and dependence structures through Bayesian model averaging. This approach avoids reliance on a single model and yields interpretable quantities for clinical decision making. We demonstrate the performance of COBRA-DOSE using DEN-181, a phase I immunology trial in rheumatoid arthritis. We also provide a general implementation of our approach through the CobraDose package in R.

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Luke Hagar, Min Zhang, Ranjeny Thomas, Andrew J. Martin. 2026-08-13. COBRA-DOSE: Copula-based Bayesian Model Averaging for Dose Selection. https://arxiv.org/abs/2608.13423

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