# Learning Survival Models with Right-Censored Reporting Delays

arXiv: https://arxiv.org/abs/2510.04421 · HTML: https://mlsanity.com/paper/2510.04421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Linear Bandits with Stochastic Delayed Feedback](https://mlsanity.com/paper/1807.02089/) (2018)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays](https://mlsanity.com/paper/2308.10675/) (2023)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [Multivariate Hierarchical Frameworks for Modelling Delayed Reporting in Count Data](https://mlsanity.com/paper/1904.03397/) (2019)
- [Multi-Source Survival Domain Adaptation](https://mlsanity.com/paper/2212.00424/) (2022)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)

## Abstract (arXiv)

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.