# Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores

arXiv: https://arxiv.org/abs/2312.05854 · HTML: https://mlsanity.com/paper/2312.05854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [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)
- [Deep Survival Analysis](https://mlsanity.com/paper/1608.02158/) (2016)
- [Gaussian Processes for Survival Analysis](https://mlsanity.com/paper/1611.00817/) (2016)
- [Deep Exponential Families](https://mlsanity.com/paper/1411.2581/) (2014)
- [An Efficient Training Algorithm for Kernel Survival Support Vector Machines](https://mlsanity.com/paper/1611.07054/) (2016)
- [A Predictive Approach to Bayesian Nonparametric Survival Analysis](https://mlsanity.com/paper/2202.10361/) (2022)

## Abstract (arXiv)

Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time. In this work, we show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior of a given population, and (2) independently distributed survival scores, which model idiosyncratic probabilistic dynamics of its given members, in a fully parametric setting. The proposed inference method is shown to dynamically handle right-censored observation horizons, and to achieve competitive performance when compared to other state-of-the-art methods in a variety of real-world datasets, including computationally inefficient Deep Learning-based SA methods and models that require MCMC for inference. Nevertheless, our method achieves robust results from the outset, while not being subjected to fine-tuning or hyperparameter optimization.