Survival analysis provides statistical methods to model the time until an event occurs. Reporting delays arise when event times are not observed at their occurrence but are only revealed upon reporting. This issue is particularly critical for timely risk evaluation when the observation window is short due to administrative censoring. In this study, we incorporate right-censored reporting delays by jointly modeling parametric hazards for the event and reporting processes. We then construct a consistent estimator for the model parameters and develop a Monte Carlo expectation-maximization algorithm to compute it. To address the challenges posed by administrative censoring, we leverage these findings and propose a transfer-learning procedure. Experimental results demonstrate that our method improves the accuracy of timely risk evaluation under administrative censoring.

翻译：生存分析提供了对事件发生时间进行建模的统计方法。当事件发生时间未在发生时被观测到，而是仅在报告后才被揭示时，便会产生报告延迟。这一问题在因行政删失导致观测窗口较短的情况下，对及时的风险评估尤为关键。在本研究中，我们通过对事件和报告过程的参数风险函数进行联合建模，将右删失报告延迟纳入考虑。随后，我们构建了模型参数的一致估计量，并开发了一种蒙特卡洛期望最大化算法进行计算。为应对行政删失带来的挑战，我们利用这些发现提出了一种迁移学习流程。实验结果表明，我们的方法提高了行政删失情况下及时风险评估的准确性。