We propose a novel method that simultaneously determines the sample size for testing two survival probabilities at a pre-specified ltime while guaranteeing type I error control in both fixed and group-sequential trial designs. Simulations across varying hypothesized differences, failure distributions, censoring proportions, and nominal powers demonstrate consistent performance, while interim analyses highlight reduced type I error and increased power at each look, regardless of the underlying failure time distribution or spending function. Importantly, our method is especially useful for evaluating survival outcomes at a fixed time in randomized trials where one treatment arm includes neoadjuvant therapy prior to surgery while the other involves surgery alone. Furthermore, it is advantageous when the proportional hazards assumption is not satisfied, as often occurs in immunotherapy trials with delayed or time-varying treatment effects or crossing survival curves. The method is also applicable to randomized phase II trials, where smaller sample sizes and the use of intermediate or surrogate time-to-event endpoints demand efficient data use and robust error control. We illustrate the approach with motivating examples in renal and prostate cancer. An accompanying R Shiny application enables investigators to compute sample sizes interactively, facilitating practical trial planning in diverse settings.

翻译：本文提出了一种新方法，可同时确定检验两个预设时间点生存概率所需的样本量，同时保证固定设计和成组序贯试验设计中的I类错误控制。跨不同假设差异、失效分布、删失比例和名义功效的模拟结果表明该方法性能稳健，而期中分析则显示：无论潜在的失效时间分布或消耗函数如何，每次分析时I类错误均降低且功效提升。重要的是，该方法特别适用于随机试验中评估固定时间点的生存结局，尤其当某治疗组采用术前新辅助治疗而对照组仅接受手术时。此外，当比例风险假设不成立时（常见于具有延迟或时变治疗效应、或生存曲线交叉的免疫疗法试验），该方法同样具有优势。该方法也适用于随机化II期试验，这类试验因样本量较小且使用中间或替代时间至事件终点，需要高效数据利用和稳健误差控制。我们通过肾癌和前列腺癌的激励性案例阐述了该方法。配套的R Shiny应用程序可使研究者交互式计算样本量，促进不同场景下的试验规划实践。