Scikit Survival

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.

Current release and installation

Verified 2026-07-23:

• 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.

Create an isolated environment and install the tested snapshot:

uv venv --python 3.11
source .venv/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.0" \
  "numpy==2.4.6" \
  "pandas==3.0.5" \
  "scipy==1.17.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.5.3" \
  "numexpr==2.14.2" \
  "narwhals==2.24.0"

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. Use references/data-handling.md before loading custom or competing-risk data.

Leakage-safe pipeline

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sksurv.linear_model import CoxPHSurvivalAnalysis

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y["event"], random_state=20260723
)

preprocess = ColumnTransformer(
    [
        ("num", make_pipeline(SimpleImputer(strategy="median"), StandardScaler()), numeric),
        (
            "cat",
            make_pipeline(
                SimpleImputer(strategy="most_frequent"),
                OneHotEncoder(handle_unknown="ignore", drop="first", sparse_output=False),
            ),
            categorical,
        ),
    ],
    sparse_threshold=0.0,
)
model = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties="efron"))
model.fit(X_train, y_train)
risk = model.predict(X_test)

The split precedes every learned transformation. For repeated or grouped records, use a group-aware split; for temporal deployment, use a time-respecting split.

Model choice

• CoxPHSurvivalAnalysis: interpretable log-hazard coefficients under proportional hazards; alpha is ridge shrinkage and ties is "breslow" or "efron".
• CoxnetSurvivalAnalysis: LASSO/elastic-net path for high-dimensional data. l1_ratio is in (0, 1]; use fit_baseline_model=True before requesting survival or cumulative-hazard functions.
• IPCRidge: IPC-weighted ridge AFT model; prediction is on a time/log-time scale, not a Cox risk score.
• RandomSurvivalForest / ExtraSurvivalTrees: nonlinear survival and cumulative hazard predictions; use permutation importance, not impurity importance.
• GradientBoostingSurvivalAnalysis: tree boosting with "coxph", "squared", or "ipcwls" loss. criterion was removed in 0.28.
• ComponentwiseGradientBoostingSurvivalAnalysis: sparse linear componentwise boosting.
• FastSurvivalSVM / FastKernelSurvivalSVM: ranking or regression objectives. Only rank_ratio=1 directly returns higher-is-riskier scores; SVMs do not yield survival probabilities for Brier metrics.

Read the model-specific reference before interpreting coefficients or predictions: references/cox-models.md, references/ensemble-models.md, or references/svm-models.md.

Prediction and metric contracts

import numpy as np
from sksurv.metrics import (
    brier_score,
    concordance_index_ipcw,
    cumulative_dynamic_auc,
    integrated_brier_score,
)

risk = model.predict(X_test)  # (n_test,), higher means higher event risk
uno_c = concordance_index_ipcw(y_train, y_test, risk, tau=times[-1])[0]
auc_t, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk, times)

surv_fns = model.predict_survival_function(X_test)
surv_prob = np.vstack([fn(times) for fn in surv_fns])  # (n_test, n_times)
_, brier_t = brier_score(y_train, y_test, surv_prob, times)
ibs = integrated_brier_score(y_train, y_test, surv_prob, times)

• Harrell C and Uno C measure rank discrimination, not calibration.
• Cumulative/dynamic AUC measures discrimination at selected horizons and accepts 1D or time-dependent 2D risk scores; it rejects survival probabilities.
• Brier score is censoring-weighted probability error and reflects both discrimination and calibration. It is not a standalone calibration curve.
• Calibration requires horizon-specific predicted-versus-observed checks on independent data. scikit-survival 0.28 has no dedicated calibration-curve API.

See references/evaluation-metrics.md for assumptions, primary literature, safe time-grid construction, and scorer wrappers.

Pipelines, metadata routing, and tuning

Ordinary Pipeline.fit(X, y) needs no metadata-routing setup. Metric wrappers such