# A review on competing risks methods for survival analysis

arXiv: https://arxiv.org/abs/2212.05157 · HTML: https://mlsanity.com/paper/2212.05157/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [Neural Fine-Gray: Monotonic neural networks for competing risks](https://mlsanity.com/paper/2305.06703/) (2023)
- [Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks](https://mlsanity.com/paper/2410.16765/) (2024)
- [Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks](https://mlsanity.com/paper/2406.14085/) (2024)
- [Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes](https://mlsanity.com/paper/2411.12159/) (2024)

## Abstract (arXiv)

When modelling competing risks survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of competing risks survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.