COBRA-DOSE：基于Copula的贝叶斯模型平均剂量选择方法
COBRA-DOSE: Copula-based Bayesian Model Averaging for Dose Selection
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中文总结 AI 辅助
COBRA-DOSE是基于Copula的贝叶斯模型平均框架，用于小样本剂量选择，通过R包实现，在类风湿关节炎I期试验中验证了其性能，可辅助临床决策。
中文摘要 AI 辅助
早期用于剂量选择的临床试验通常招募少量患者，旨在识别出既安全又有进一步研究潜力的剂量。传统方法会确定最大耐受剂量，而针对靶向疗法的现代试验往往追求最优生物剂量，即达到足够生物活性且安全性可接受的最低剂量。在免疫学场景中，生物活性评估基于多个生物标志物或临床终点，因此主导剂量选择工作的临床医生会受益于对各剂量下生物标志物结果组合概率的透明总结。然而，在小样本情况下进行此类推断颇具挑战，因为复杂的建模假设难以验证。为解决这一局限，我们提出COBRA-DOSE，这是一种基于两个终点的后验预测推断框架，通过Copula建模依赖关系，并利用贝叶斯模型平均考量边缘分布与依赖结构中的不确定性。该方法避免依赖单一模型，能产生可解释的临床决策量。我们使用DEN-181（一项针对类风湿关节炎的I期免疫学试验）验证了COBRA-DOSE的性能，还通过R语言中的CobraDose包提供了该方法的通用实现。
英文摘要
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.
