# A reproducible and extensible framework for benchmarking competing risks survival models

arXiv: https://arxiv.org/abs/2608.00271 · HTML: https://mlsanity.com/paper/2608.00271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 68 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Position: Stop Chasing the C-index when Evaluating Survival Analysis Models](https://mlsanity.com/paper/2506.02075/) (2025)
- [A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data](https://mlsanity.com/paper/2406.04098/) (2024)
- [Competing Risks: Impact on Risk Estimation and Algorithmic Fairness](https://mlsanity.com/paper/2508.05435/) (2025)
- [Comparative Review of Modern Competing Risk Methods in High-dimensional Settings](https://mlsanity.com/paper/2503.12824/) (2025)
- [The C-index Multiverse](https://mlsanity.com/paper/2508.14821/) (2025)
- [On the calibration of survival models with competing risks](https://mlsanity.com/paper/2602.00194/) (2026)

## Abstract (arXiv)

A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.e., cancer death) precludes the occurrence of other events (i.e., cardiovascular disease death). Despite these methodological advances, their systematic evaluation and adoption are limited by the lack of comprehensive, reproducible and extensible benchmarking frameworks. We developed an open-source benchmarking framework for competing risks models that enables their systematic comparison across multiple datasets under different aspects of performance; calibration, discrimination, overall prediction error and clinical utility. We additionally introduce an extension of SHAP for competing risks, allowing model-agnostic interpretability of covariates contributions over time. All our code is publicly available via GitHub:https://github.com/BBolosSierra/CompRisksBenchmark