The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the $\textit{status quo}$ of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/.

翻译：美国食品药品监督管理局（FDA）肿瘤卓越中心的“Optimus计划”被广泛视为一项开创性举措，旨在改变肿瘤学传统剂量探索策略的现状。与其他治疗领域在剂量范围研究中充分评估多个剂量不同，早期肿瘤剂量探索研究的特征是确定单一剂量，例如最大耐受剂量（MTD）或推荐二期剂量（RP2D）。遵循Optimus计划的理念，我们提出了一种多臂两阶段（MATS）设计，用于概念验证（PoC）和剂量优化，该设计允许评估来自剂量递增试验的两个选定剂量。该设计在第一阶段跨多个适应症优先评估较高剂量，若较高剂量展现出有效的抗肿瘤活性，则针对该适应症自适应地进入第二阶段。在第二阶段，通过较高剂量与较低剂量的随机比较，实现概念验证（PoC）和剂量优化。通过跨剂量、适应症和阶段的信息借入，一个贝叶斯层次模型控制统计推断和决策。我们的模拟研究表明，所提出的MATS设计产生了理想性能。已开发了一个R Shiny应用程序，可通过https://matsdesign.shinyapps.io/mats/ 访问。