A reproducible and extensible framework for benchmarking competing risks survival models

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:this https URL

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2608.00271 [stat.ML]

(or arXiv:2608.00271v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2608.00271 Focus to learn more arXiv-issued DOI via DataCite

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