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scikit-survival

Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.

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# scikit-survival

## Scope

Use this skill for scikit-survival 0.28.0 workflows involving:

- right-censored structured outcomes;
- Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;
- discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;
- nonparametric cumulative incidence with competing risks;
- scikit-learn pipelines, nested model selection, and reproducible reports.

scikit-survival primarily models right-censored outcomes. Its built-in competing-risk
support is nonparametric cumulative incidence; it does not provide Fine-Gray regression.