This work revisits optimal response-adaptive designs from a type-I error rate perspective, highlighting when and how much these allocations exacerbate type-I error rate inflation - an issue previously undocumented. We explore a range of approaches from the literature that can be applied to reduce type-I error rate inflation. However, we found that all of these approaches fail to give a robust solution to the problem. To address this, we derive two optimal proportions, incorporating the more robust score test (instead of the Wald test) with finite sample estimators (instead of the unknown true values) in the formulation of the optimization problem. One proportion optimizes statistical power and the other minimizes the total number failures in a trial while maintaining a predefined power level. Through simulations based on an early-phase and a confirmatory trial we provide crucial practical insight into how these new optimal proportion designs can offer substantial patient outcomes advantages while controlling type-I error rate. While we focused on binary outcomes, the framework offers valuable insights that naturally extend to other outcome types, multi-armed trials and alternative measures of interest.

翻译：本研究从I类错误率视角重新审视响应自适应最优设计，首次系统揭示了此类分配方案加剧I类错误率膨胀的具体条件与程度。我们系统评估了现有文献中可用于缓解I类错误率膨胀的多种方法，但发现所有方法均未能提供稳健的解决方案。为此，我们通过将稳健性更强的得分检验（替代Wald检验）与有限样本估计量（替代未知真值）纳入优化问题框架，推导出两种最优比例方案：一种方案优化统计功效，另一种方案在维持预设功效水平的前提下最小化试验总体失败病例数。基于早期试验与确证性试验的模拟研究显示，这些新型最优比例设计在控制I类错误率的同时，能为患者结局带来显著改善。虽然本研究聚焦二元结局，但所提框架可自然拓展至其他结局类型、多臂试验及不同目标度量体系。