In oncology phase I trials, model-assisted designs have been increasingly adopted because they enable adaptive yet operationally simple dose adjustment based on accumulating safety data, leading to a paradigm shift in dose-escalation methodology. In practice, a single mid-trial dose insertion may be considered to examine safer doses and/or to collect more informative efficacy data. In this study, we investigate methods to improve dose assignment and the selection of the maximum tolerated dose (MTD) or the optimal biological dose (OBD) when a new dose level is added during an ongoing trial under a model-assisted framework, by assigning informative prior information to the inserted dose. We propose a hybrid design that uses a non-informative model-assisted design at trial initiation and, upon dose insertion, applies an informative-prior extension only to the newly added dose. In addition, to address potential skeleton misspecification, we propose two adaptive extensions: (i) an online-weighting approach that updates the skeleton over time, and (ii) a Bayesian-mixture approach that robustly combines multiple candidate skeletons. We evaluate the proposed methods through simulation studies.

翻译：在肿瘤学I期临床试验中，模型辅助设计因其能够基于累积的安全性数据进行自适应且操作简便的剂量调整而日益被采用，这导致了剂量递增方法论的范式转变。在实践中，可能会考虑在试验中期插入单个新剂量水平，以考察更安全的剂量和/或收集更具信息量的疗效数据。在本研究中，我们探讨在模型辅助框架下，当正在进行的试验中添加新剂量水平时，通过为插入的剂量分配信息性先验信息，以改进剂量分配及最大耐受剂量（MTD）或最佳生物学剂量（OBD）选择的方法。我们提出一种混合设计，该设计在试验启动时使用非信息性模型辅助设计，并在剂量插入时，仅对新添加的剂量应用信息性先验扩展。此外，针对可能存在的骨架误设问题，我们提出了两种自适应扩展方法：（i）一种随时间更新骨架的在线加权方法，以及（ii）一种稳健结合多个候选骨架的贝叶斯混合方法。我们通过模拟研究对所提方法进行了评估。