The conventional more-is-better dose selection paradigm, which targets the maximum tolerated dose (MTD), is not suitable for the development of targeted therapies and immunotherapies as the efficacy of these novel therapies may not increase with the dose. The U.S. Food and Drug Administration (FDA) has launched Project Optimus "to reform the dose optimization and dose selection paradigm in oncology drug development", and recently published a draft guidance on dose optimization, which outlines various approaches to achieve this goal. One highlighted approach involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This paper focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We propose a MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy) design that can be easily implemented with pre-calculated decision boundaries included in the protocol. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. Simulation studies demonstrate that the resulting MERIT design has desirable operating characteristics. To facilitate the implementation of the MERIT design, we provide software, available at www.trialdesign.org.

翻译：传统的“越多越好”的剂量选择范式以最大耐受剂量为目标，不适用于靶向治疗和免疫治疗，因为这些新型疗法的疗效可能不随剂量增加而提高。美国食品药品监督管理局已启动“优化计划”，旨在“改革肿瘤药物开发中的剂量优化与剂量选择范式”，并最近发布了关于剂量优化的指南草案，概述了实现该目标的各种方法。其中一种突出的方法是在Ⅰ期试验完成后开展随机化Ⅱ期试验，比较多个剂量（通常包括最大耐受剂量及低于该剂量的一至两个剂量），以确定最大化获益-风险权衡的最佳剂量。本文聚焦于此类多剂量随机化试验的设计，特别是样本量的确定。我们提出了一种基于毒性和疗效的剂量优化多剂量随机化试验设计方案，该方案可通过方案中预先计算的决策边界轻松实施。我们将Ⅰ类错误和检验效力的标准定义推广至剂量优化的独特特性，并推导了决策规则及确定最佳样本量的算法。模拟研究表明，该设计方案具有理想的操作特性。为促进该设计方案的实施，我们提供了软件，网址为 www.trialdesign.org。