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The Project Optimus initiative by the FDA's Oncology Center of Excellence iswidely viewed as a groundbreaking effort to change the $\textit{status quo}$ ofconventional dose-finding strategies in oncology. Unlike in other therapeuticareas where multiple doses are evaluated thoroughly in dose ranging studies,early-phase oncology dose-finding studies are characterized by the practice ofidentifying a single dose, such as the maximum tolerated dose (MTD) or therecommended phase 2 dose (RP2D). Following the spirit of Project Optimus, wepropose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) anddose optimization that allows the evaluation of two selected doses from adose-escalation trial. The design assess the higher dose first across multipleindications in the first stage, and adaptively enters the second stage for anindication if the higher dose exhibits promising anti-tumor activities. In thesecond stage, a randomized comparison between the higher and lower doses isconducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesianhierarchical model governs the statistical inference and decision making byborrowing information across doses, indications, and stages. Our simulationstudies show that the proposed MATS design yield desirable performance. An RShiny application has been developed and made available athttps://matsdesign.shinyapps.io/mats/.

A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials