生存分析是一种有价值的工具，可根据基线观察来估计特定事件（例如死亡或癌症复发）之前的时间。这在医疗保健领域特别有用，可以根据患者数据对临床重要事件进行预后预测。然而，现有的方法往往有局限性；有些只关注按生存率对患者进行排名，忽略估计实际事件时间，而另一些则将问题视为分类任务，忽略事件固有的时间顺序结构。此外，有效利用审查样本（事件时间未知的数据点）对于提高模型的预测准确性至关重要。在本文中，我们介绍了 CenTime，这是一种直接估计事件发生时间的生存分析新方法。我们的方法具有创新的事件条件审查机制，即使在未经审查的数据稀缺的情况下也能稳健地执行。我们证明，即使在没有未经审查的数据的情况下，我们的方法也可以形成事件模型参数的一致估计器。此外，CenTime 可以轻松与深度学习模型集成，并且对批量大小或未经审查的样本数量没有限制。我们将我们的方法与标准生存分析方法进行比较，包括 Cox 比例风险模型和 DeepHit。我们的结果表明，CenTime 在预测死亡时间方面提供了最先进的性能，同时保持了可比较的排名性能。我们的实现可在 https://github.com/ahmedhshahin/CenTime.Copyright © 2023 作者公开获取。由 Elsevier B.V. 出版。保留所有权利。

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. Additionally, the effective utilisation of censored samples-data points where the event time is unknown- is essential for enhancing the model's predictive accuracy. 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 to 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.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.