Early-phase dose-finding clinical trials are often subject to the issue of late-onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occurrence of the first outcome will terminate the other one. In this paper, we propose a novel Bayesian adaptive phase I/II clinical trial design to address the issue of late-onset competing risk outcomes. We use the continuation-ratio model to characterize the trinomial response outcomes and the cause-specific hazard rate method to model the competing-risk survival outcomes. We treat the late-onset outcomes as missing data and develop a Bayesian data augmentation method to impute the missing data from the observations. We also propose an adaptive dose-finding algorithm to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed design yields desirable operating characteristics.

中文翻译：

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具有迟发性竞争风险结果的贝叶斯自适应 I/II 期临床试验设计

早期剂量寻找临床试验通常会受到迟发结果的影响。在 I/II 期临床试验中，这个问题变得更加棘手，因为毒性和疗效可能是相互竞争的风险结果，因此第一个结果的发生将终止另一个结果。在本文中，我们提出了一种新颖的贝叶斯自适应 I/II 期临床试验设计，以解决迟发性竞争风险结果的问题。我们使用持续比率模型来表征三项式响应结果，并使用特定原因的风险率方法来模拟竞争风险生存结果。我们将迟发的结果视为缺失数据，并开发了一种贝叶斯数据增强方法来从观察中估算缺失的数据。我们还提出了一种自适应剂量寻找算法来分配患者并在试验期间确定最佳生物剂量。仿真研究表明，所提出的设计产生了理想的操作特性。