Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Furthermore, the effective utilization of censored samples - training data points where the exact event time is unknown - is essential for improving the predictive accuracy of the model. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach with standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.

翻译：生存分析是一种基于基线观测数据估计特定事件（如死亡或癌症复发）发生时间的有效工具，在医疗领域具有重要应用价值，可通过患者数据预测临床关键事件的发生时间。然而现有方法往往存在局限性：部分方法仅关注患者生存排序而忽略实际事件时间估计，另一些方法将问题视为分类任务而忽视事件内在的时间顺序结构。此外，有效利用删失样本（无法获知确切事件时间的训练数据点）对于提升模型预测精度至关重要。本文提出CenTime这一新型生存分析方法，可直接估计事件发生时间。该方法创新性地引入事件条件删失机制，在非删失数据稀缺时仍能保持稳健性能。我们证明，即便完全缺失非删失数据，该方法的估计量仍能保持事件模型参数的一致性。CenTime可轻松集成至深度学习模型，且对批大小或非删失样本数量无限制。通过与Cox比例风险模型、DeepHit等标准生存分析方法的对比实验，结果表明CenTime在预测死亡时间方面达到最先进水平，同时保持可比的排序性能。本方法的实现代码已公开于https://github.com/ahmedhshahin/CenTime。