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

2026-08-13

AI Summary

Selecting the optimal biological dose in early-phase clinical trials with limited sample sizes, based on multiple biomarkers and safety data, remains a significant challenge. This work proposes COBRA-DOSE, a novel framework that uniquely integrates copula modeling with Bayesian model averaging to jointly characterize the dependence structure among multiple endpoints and their marginal distributions, while simultaneously accounting for uncertainty in both components. The approach yields robust, interpretable probabilistic dose evaluations and supports clinical decision-making through posterior predictive inference. Its effectiveness is demonstrated in the DEN-181 phase I trial for rheumatoid arthritis, and an open-source R package, CobraDose, is provided to facilitate practical implementation.

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.

Problem

• dose selection
• optimal biological dose
• multiple endpoints
• small sample inference
• clinical trial

Innovation

• Copula
• Bayesian model averaging
• dose selection
• posterior predictive inference
• optimal biological dose