# Survival stacking: casting survival analysis as a classification problem

arXiv: https://arxiv.org/abs/2107.13480 · HTML: https://mlsanity.com/paper/2107.13480/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2021.

## Built on by (most cited first, 13 of 13)

- [An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes](https://mlsanity.com/paper/2410.01086/) (2024)
- [Temporal Label Smoothing for Early Event Prediction](https://mlsanity.com/paper/2208.13764/) (2022)
- [Survival Mixture Density Networks](https://mlsanity.com/paper/2208.10759/) (2022)
- [Interpretable Survival Analysis for Heart Failure Risk Prediction](https://mlsanity.com/paper/2310.15472/) (2023)
- [Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks](https://mlsanity.com/paper/2603.29475/) (2026)
- [A Federated Cox Model with Non-Proportional Hazards](https://mlsanity.com/paper/2207.05050/) (2022)
- [Interpretable Prediction and Feature Selection for Survival Analysis](https://mlsanity.com/paper/2404.14689/) (2024)
- [CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning](https://mlsanity.com/paper/2502.03946/) (2025)
- [Deep End-to-End Survival Analysis with Temporal Consistency](https://mlsanity.com/paper/2410.06786/) (2024)
- [Confidence intervals for the Cox model test error from cross-validation](https://mlsanity.com/paper/2201.10770/) (2022)
- [Une comparaison des algorithmes d'apprentissage pour la survie avec donn'ees manquantes](https://mlsanity.com/paper/2303.13590/) (2023)
- [Reduction Techniques for Survival Analysis](https://mlsanity.com/paper/2508.05715/) (2025)
- [A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature](https://mlsanity.com/paper/2605.16208/) (2026)

## Abstract (arXiv)

While there are many well-developed data science methods for classification and regression, there are relatively few methods for working with right-censored data. Here, we present "survival stacking": a method for casting survival analysis problems as classification problems, thereby allowing the use of general classification methods and software in a survival setting. Inspired by the Cox partial likelihood, survival stacking collects features and outcomes of survival data in a large data frame with a binary outcome. We show that survival stacking with logistic regression is approximately equivalent to the Cox proportional hazards model. We further recommend methods for evaluating model performance in the survival stacked setting, and we illustrate survival stacking on real and simulated data. By reframing survival problems as classification problems, we make it possible for data scientists to use well-known learning algorithms (including random forests, gradient boosting machines and neural networks) in a survival setting, and lower the barrier for flexible survival modeling.