Model-assisted interval designs such as the Keyboard design are transparent and easy to implement in phase I oncology trials. However, interim decisions based solely on data from the current dose may overlook informative signals from neighbouring doses, leading to unnecessary escalation or de-escalation. We propose the shared Keyboard design, a Bayesian model-assisted design that replaces the independent beta--binomial updating scheme at each dose with a posterior induced by a Beta kernel process using kernel-weighted pseudo-counts. The design preserves the decision structure of the Keyboard design while enabling controlled borrowing across nearby doses. To prioritise overdose control, we propose an asymmetric kernel that assigns greater weight to toxicities observed at higher doses during escalation. We further extend the proposed design to accommodate adaptive dose insertion when the initial dose grid is inadequate and time-to-event outcomes when late-onset toxicities are present. Extensive simulation studies demonstrate substantial improvements in both accuracy and safety for identifying the maximum tolerated dose. In settings involving dose insertion, the proposed design identifies inserted target doses more effectively than adaptive dose modification while maintaining a comparable modification rate.

翻译：模型辅助的区间设计（如Keyboard设计）在I期肿瘤学试验中具有操作透明、易于实施的优点。然而，仅基于当前剂量数据进行的中期决策可能忽略相邻剂量的信息性信号，导致不必要的剂量递增或递减。我们提出共享Keyboard设计——一种贝叶斯模型辅助设计，用基于核加权伪计数的Beta核过程后验取代各剂量独立的Beta-二项更新方案。该设计在保留Keyboard设计决策结构的同时，实现了对邻近剂量的受控信息借用。为优先控制过量风险，我们提出非对称核函数，在剂量递增时为较高剂量观察到的毒性反应赋予更大权重。进一步扩展该设计，使其能处理初始剂量网格不充分时的自适应剂量插入，以及存在迟发性毒性的时间-事件结果。大量模拟研究表明，该方法在确定最大耐受剂量时的准确性和安全性均有显著提升。在涉及剂量插入的场景中，该设计在保持相当修改率的同时，比自适应剂量修改策略更有效地识别插入目标剂量。