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. The corresponding operating characteristics depend on 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. Ensuring adequate follow-up to accrue the required events is therefore critical, making interim prediction of information at scheduled looks and at the final analysis 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 an 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 lost 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效果的核心指标。许多试验方案采用组序贯设计中的中期分析，通过消耗函数或边界方法控制Ⅰ类错误。相应的操作特征取决于观察次数和累积信息量。对于时间-事件终点的中期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。确保充分的随访时间以积累所需事件数至关重要，这使得对预定观察时点和最终分析时的信息量进行中期预测成为必要。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成熟框架。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中期监测场景。该适应性改进为临床环境中的事件计数预测区间提供了通用框架，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、阶梯式入组以及入组日期与失访之间可能存在的依赖关系。预测区间通过贝叶斯框架下的自助法估计器获得未来事件条件分布。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真实Ⅲ期试验的分析加以验证。