🤖 AI Summary
This work addresses the lack of a systematic, reproducible, and scalable evaluation benchmark for competing risks survival analysis. It introduces the first open-source benchmarking framework specifically designed for competing risks models, enabling comprehensive assessment across multiple datasets along key dimensions including calibration, discrimination, overall prediction error, and clinical utility. The framework further advances model interpretability by innovatively extending SHAP (SHapley Additive exPlanations) to support time-dynamic, model-agnostic explanations. Compatible with a wide range of modeling approaches and accompanied by publicly released code, this benchmark significantly promotes standardized evaluation and interpretable deployment of competing risks models in practice.
📝 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