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. Do not present model output as clinical advice, causal evidence, or proof of clinical utility.

Python snippets using study-defined variables such as X_train, y_train, frame, or times are illustrative integration templates. The native regression tests and local CLI smoke runs use small synthetic fixtures; they do not validate an unprovided study, clinical dataset, or every optional dataframe backend.

Current release and installation

Verified 2026-10-01:

• Latest stable: scikit-survival 0.28.0, released 2026-07-05.
• Python: 3.11 or later; PyPI wheels cover CPython 3.11-3.14 on Linux x86-64, macOS x86-64/ARM64, and Windows x86-64.
• Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0, scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1.
• 0.28 adds pandas/Polars estimator support through narwhals and removes criterion from GradientBoostingSurvivalAnalysis.

The native refresh tests used Python 3.13 and the following snapshot (NumPy 2.5.1 was cached; this is a tested snapshot, not a claim that every pin is newest). Create an isolated environment:

uv venv .venv-survival --python 3.13
source .venv-survival/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.1" \
  "numpy==2.5.1" \
  "pandas==3.0.6" \
  "scipy==1.18.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.6.0" \
  "numexpr==2.14.2" \
  "narwhals==2.26.0"

Binary wheels are preferred. A source build requires a C/C++ compiler; OSQP may also require CMake. This skill is MIT-licensed; the upstream scikit-survival package is GPL-3.0-or-later, so review upstream licensing before redistribution.

Non-negotiable workflow

1. Define the estimand and event coding. Decide whether the target is all-event survival, cause-specific hazard, or cause-specific cumulative incidence.
2. Validate outcomes. Standard estimators need a two-field structured array: boolean event first, observed time second. Competing-risk CIF instead needs a separate integer event vector: 0=censored, 1..K=causes.
3. Split before learned preprocessing. Never fit imputers, encoders, scalers, feature selectors, or alpha choices on all rows before splitting.
4. Fit preprocessing inside a pipeline. Unknown categories and missingness must be handled using training-fold state only.
5. Tune without reusing evaluation data. Use nested CV when reporting cross-validated tuned performance, or reserve a truly untouched final holdout.
6. Fit censoring distributions on training data. IPCW concordance, dynamic AUC, and Brier metrics receive survival_train, never a pooled train+test outcome.
7. Restrict evaluation times. Use a strictly increasing grid inside test follow-up and below the end of training support where the estimated censoring survival remains positive.
8. Match predictions to metrics. Concordance/dynamic AUC consume higher-is-riskier scores. Brier metrics consume survival probabilities with shape (n_test, n_times), not risk scores or unevaluated step functions.
9. Handle competing causes explicitly. Standard survival probabilities and CIFs answer different questions. Never estimate event-specific probability with 1 - Kaplan-Meier while censoring competing events.
10. Report limits. Separate discrimination, calibration, prediction error, and cumulative incidence. None alone establishes decision or clinical utility.

Outcome construction

from sksurv.util import Surv

y = Surv.from_arrays(event=event_bool, time=observed_time)
# Equivalent for pandas or Polars:
y = Surv.from_dataframe("event", "time", frame)

The first field is boolean (True=event, False=right-censored); the second is floating-point time. Field names may vary, but field order and meaning may not.

…