Time-to-event endpoints are central to evaluate treatment efficacy across many disease areas. Many trial protocols include interim analyses within group-sequential designs that control type I error via spending functions or boundary methods, with operating characteristics determined by the number of looks and the information accrued. Planning interim analyses with time-to-event endpoints is challenging because statistical information depends on the number of observed events, so adequate follow-up to accrue the required events is critical and interim prediction of information at scheduled looks and at the final analysis becomes essential. While several methods have been developed to predict the calendar time required to reach a target number of events, to the best of our knowledge there is no established framework that addresses the prediction of the number of events at a future date with corresponding prediction intervals. Starting from prediction interval approach originally developed in reliability engineering for the number of future component failures, we reformulated and extended it to the context of interim monitoring in clinical trials. This adaptation yields a general framework for event-count prediction intervals in the clinical setting, taking the patient as the unit of analysis and accommodating a range of parametric survival models, patient-level covariates, stagged entry and possible dependence between entry dates and loss to follow-up. Prediction intervals are obtained in a frequentist framework from a bootstrap estimator of the conditional distribution of future events. The performance of the proposed approach is investigated via simulation studies and illustrated by analyzing a real-world phase III trial in childhood acute lymphoblastic leukaemia.

翻译：时间-事件终点是评估多种疾病领域治疗疗效的核心指标。许多试验方案采用组序贯设计进行期中分析，通过消耗函数或边界方法控制Ⅰ类错误，其操作特征由观察次数和累积信息量决定。对于时间-事件终点的期中分析规划具有挑战性，因为统计信息量取决于观察到的事件数量，因此必须通过充分随访来积累所需事件，而对预定观察时点和最终分析时的信息量进行期中预测变得至关重要。虽然已有多种方法用于预测达到目标事件数所需的日历时间，但据我们所知，目前尚未建立能够预测未来日期事件数量并提供相应预测区间的系统框架。基于可靠性工程中为预测未来部件故障数量而开发的预测区间方法，我们将其重新构建并扩展至临床试验的期中监测场景。这一改编为临床环境中的事件计数预测区间建立了通用框架，以患者为分析单元，兼容多种参数生存模型、患者层面协变量、交错入组以及入组日期与失访之间可能存在的依赖关系。预测区间在频率学框架下通过未来事件条件分布的自举估计量获得。通过模拟研究评估了所提方法的性能，并通过对儿童急性淋巴细胞白血病的真实世界Ⅲ期试验分析进行例证。