A reproducible and extensible framework for benchmarking competing risks survival models
Begoña B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos
stat.ML, cs.LG
Submitted: 2026-07-31
Comments: 23 pages main text (7 figures), 31 pages supplementary information
Code: https://github.com/BBolosSierra/CompRisksBenchmark
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
Abstract
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
Sources
- On the calibration of survival models with competing risks
- A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
- Comparative Review of Modern Competing Risk Methods in High-dimensional Settings
- Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
- The C-index Multiverse
Related papers
- Behavior of prediction performance metrics with rare events
- Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
- A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
- Scalable Posterior Uncertainty for Flexible Density-Based Clustering
- A Transport Geometry of Benign Overfitting
- Efficient quadratic entropy with distance sketches