The FDA's Project Optimus initiative emphasizes patient-centered dose selection in oncology that balances efficacy and safety. We develop a framework for randomized dose optimization studies that uses clinically interpretable utility scores to integrate binary efficacy and safety endpoints and select the optimal dose for a follow-on confirmatory trial. The framework provides: (i) a systematic method for eliciting utility scores that reflect clinical priorities; (ii) closed-form sample size formulas to achieve prespecified Probabilities of Correct Selection (PCS) under clinically relevant scenarios; and (iii) analytical expressions characterizing the propagation of selection-induced bias to confirmatory trials, including time-to-event endpoints correlated with the selection endpoint. Extensive simulations (10^6 replications per scenario) confirm that the sample size methods achieve target PCS and that the bias and Type I error formulas closely match empirical estimates. An R package DoseOptDesign and an interactive Shiny application are publicly available.

翻译：美国食品药品监督管理局（FDA）的Project Optimus倡议强调肿瘤治疗中需平衡疗效与安全性的患者中心剂量选择。我们开发了一个随机剂量优化研究框架，该框架采用临床可解释的效用评分整合二元疗效与安全性终点，并选择后续验证性试验的最佳剂量。该框架提供： (i) 反映临床优先级的系统性效用评分启发方法； (ii) 在临床相关场景下实现预设正确选择概率（PCS）的闭式样本量计算公式；以及 (iii) 表征选择偏差向验证性试验（包括与选择终点相关的时间至事件终点）传播的分析表达式。大规模模拟（每场景10^6次重复）证实样本量方法可达到目标PCS，且偏差与I类错误公式与经验估计高度吻合。公开可用的R包DoseOptDesign与交互式Shiny应用程序已发布。