  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.
