Interim monitoring in time-to-event trials must balance inferential maturity with operationally meaningful timing. Event-driven designs align analyses with event accumulation but can produce substantial and unpredictable calendar delays, whereas enrollment-driven designs provide predictable timing but may rely on immature follow-up. We propose the Window-Cohort with Calibrated Follow-Up Requirement (WCR) framework, which directly parameterizes follow-up maturity through a locked cohort size and a post-lock follow-up requirement. The interim analysis is conducted after the prespecified cohort has accrued the calibrated minimum follow-up, while enrollment may continue and later patients are reserved for the final analysis. The framework distinguishes restricted follow-up for landmark survival estimands from unrestricted follow-up for proportional hazards estimands, thereby linking the effective information horizon to the estimand. Design parameters and decision thresholds are jointly calibrated through constrained optimization to control type I error and power while balancing calendar time, interim maturity, and decision-lag burden. Simulation studies motivated by a rare pediatric oncology trial show that WCR attains target operating characteristics under the calibration model and offers more stable and interpretable interim timing than conventional event-driven and enrollment-driven approaches. The methodology is implemented in the open-source R package WCRBayesDesign, available on CRAN. WCR reframes interim monitoring as an information-time alignment problem and provides a practical design strategy for single-arm trials with sparse events, slow accrual, and long-horizon endpoints.

翻译：时间至事件试验中的中期监查必须在推断成熟度与具有操作意义的时机之间取得平衡。事件驱动设计将分析时间与事件累积对齐，但可能产生显著且不可预测的日历延迟；而入组驱动设计虽能提供可预测的时机，却可能依赖不成熟的随访数据。我们提出带有校准随访要求的窗口队列（WCR）框架，该框架通过锁定队列规模与锁定后随访要求直接参数化随访成熟度。中期分析在预指定队列累积达到校准最小随访期后进行，同时入组可继续，后续患者将被保留用于最终分析。该框架区分了用于标志性生存估计的受限随访与用于比例风险估计的非受限随访，从而将有效信息时间窗与估计目标相关联。设计参数与决策阈值通过约束优化联合校准，在控制I类错误与检验效能的同时，平衡日历时间、中期成熟度及决策滞后负担。基于罕见儿童肿瘤试验的模拟研究表明，WCR在校准模型下达到目标操作特征，且相比传统事件驱动与入组驱动方法能提供更稳定且可解释的中期时机。该方法已通过开源R包WCRBayesDesign实现，可于CRAN获取。WCR将中期监查重新定义为信息时间对齐问题，并为具有稀疏事件、缓慢入组及长期终点的单臂试验提供了实用设计策略。