Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
SKILL.md
scikit-survival
Scope
Use this skill for scikit-survival 0.28.0 workflows involving:
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.
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.
Installs
0
Non-negotiable workflow
Define the estimand and event coding. Decide whether the target is
all-event survival, cause-specific hazard, or cause-specific cumulative incidence.
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.
Split before learned preprocessing. Never fit imputers, encoders, scalers,
feature selectors, or alpha choices on all rows before splitting.
Fit preprocessing inside a pipeline. Unknown categories and missingness must
be handled using training-fold state only.
Tune without reusing evaluation data. Use nested CV when reporting
cross-validated tuned performance, or reserve a truly untouched final holdout.
Fit censoring distributions on training data. IPCW concordance, dynamic AUC,
and Brier metrics receive survival_train, never a pooled train+test outcome.
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.
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.
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.
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.
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
as as_concordance_index_ipcw_scorer are estimator wrappers, not scoring=
callables:
The wrapper learns the censoring distribution from each fit fold. Prefix wrapped
parameters with estimator__. Enable scikit-learn metadata routing only when
passing extra metadata through a meta-estimator. For example, Coxnet's
set_predict_request(alpha=True) matters only when routing the alpha prediction
argument with sklearn.set_config(enable_metadata_routing=True).
Use an outer CV loop for an unbiased CV performance estimate after inner tuning.
Do not select parameters and report performance from the same folds as if external.
cif has shape (K + 1, n_times); row 0 is total risk and rows 1..K are
cause-specific cumulative incidence. Cause-specific Cox models treat other causes
as censored to estimate cause-specific hazards, but one such model's
1 - survival is not the cause-specific CIF. See references/competing-risks.md.
Bundled local CLIs
All helpers use deterministic synthetic data when no input is given. They make no
network calls, reject URLs and symlinks, bound files/rows/features, avoid unsafe
pickle loading, and lazily import scientific packages.
Use only de-identified, authorized local data. The bundled tests contain synthetic
records only and no patient data or PHI.
Security triage
SECURITY.md previously claimed this skill bundled package-shadowing files named
sklearn.py and sksurv.py. The 2026-07-23 inventory confirmed those files did
not exist; the claim was a phantom analyzer finding. This refresh adds only
descriptively named helpers and no shadow modules, environment reads, or network
calls.
Never name a project script after an imported package (including sklearn.py,
sksurv.py, numpy.py, or pandas.py), because Python may import the local file
instead of the installed library. Inspect the working directory before executing
examples copied from untrusted sources.
