Adaptive seamless phase II/III design with sequential estimation-adjusted urn model

Clinical trials are an essential component of the drug development process, providing crucial data on the efficacy and safety of new treatments. However, traditional clinical trial designs can be inefficient and ineffective, leading to increased costs and a higher risk of failure. The FDA Oncology Center of Excellence (OCE) has recently initiated Project Optimus to reform the dose selection paradigm for oncology treatments. We propose the adaptive seamless phase II/III clinical trial designs (ASD) with the sequential estimation-adjusted urn (SEU) model for randomization to achieve efficient and ethical objectives. However, the combination of ASD and SEU poses a challenge in controlling the type I error rate: ASD exerts a dual influence of multiplicity and selection; all the responses and treatment assignments are not independent due to SEU. In this paper, we investigated how to overcome these difficulties, utilize the two adaptive approaches’ advantages, and control the type I error rate. We provide a theoretical foundation for this procedure, and numerical studies demonstrate that our methods can assign more people to better treatments, leading to fewer failures while still controlling the type I error rate and preserving power.

临床试验是药物开发流程中的核心环节，可为新型治疗手段的有效性与安全性提供关键数据。然而，传统临床试验设计往往效率低下且效果欠佳，导致研发成本攀升、失败风险升高。美国食品药品监督管理局肿瘤卓越中心（FDA Oncology Center of Excellence, OCE）近期启动了优化项目（Project Optimus），旨在重塑肿瘤治疗的剂量选择范式。本文提出基于序贯估计调整urn（SEU）模型进行随机化的自适应无缝II/III期临床试验设计（ASD），以实现高效且符合伦理的试验目标。不过，自适应无缝II/III期临床试验设计与序贯估计调整urn模型的结合，在控制I类错误率方面存在挑战：一方面，自适应无缝II/III期设计兼具多重性与选择偏倚的双重影响；另一方面，由于序贯估计调整urn模型的存在，所有响应变量与治疗分配均不独立。本文针对如何克服上述难点、充分利用两种自适应方法的优势并控制I类错误率展开了研究，为该试验流程提供了理论支撑。数值模拟研究表明，所提方法可将更多受试者分配至更优治疗方案，在控制I类错误率并保留检验效能的同时，降低试验失败风险。