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值估计特定事件（如死亡或癌症复发）发生时间的有效工具，在医疗领域尤为实用，可基于患者数据对临床重要事件进行预后预测。然而，现有方法通常存在局限性：部分方法仅关注患者生存概率的排序，忽略了实际事件时间的估计；另一些方法将问题视为分类任务，忽视了事件内在的时间顺序结构。此外，有效利用删失样本（即确切事件时间未知的训练数据点）对于提升模型预测精度至关重要。本文提出CenTime——一种直接估计事件时间的创新生存分析方法。该方法采用创新的事件条件删失机制，即使在未删失数据稀缺时仍能保持稳健表现。我们证明，即便完全无未删失数据，该方法仍能构成事件模型参数的一致估计量。此外，CenTime可与深度学习模型轻松集成，对批大小或未删失样本数量无任何限制。我们将所提方法与包括Cox比例风险模型和DeepHit在内的标准生存分析方法进行比较。结果表明，CenTime在预测死亡时间方面达到最优性能，同时保持可比的排序能力。我们的实现代码已公开于https://github.com/ahmedhshahin/CenTime。