| name | scikit-survival |
| description | 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. |
| license | GPL-3.0 license |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
scikit-survival: Survival Analysis in Python
Overview
scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.
Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods).
When to Use This Skill
Use this skill when:
- Performing survival analysis or time-to-event modeling
- Working with censored data (right-censored, left-censored, or interval-censored)
- Fitting Cox proportional hazards models (standard or penalized)
- Building ensemble survival models (Random Survival Forests, Gradient Boosting)
- Training Survival Support Vector Machines
- Evaluating survival model performance (concordance index, Brier score, time-dependent AUC)
- Estimating Kaplan-Meier or Nelson-Aalen curves
- Analyzing competing risks
- Preprocessing survival data or handling missing values in survival datasets
- Conducting any analysis using the scikit-survival library
Core Capabilities
1. Model Types and Selection
scikit-survival provides multiple model families, each suited for different scenarios:
Cox Proportional Hazards Models
Use for: Standard survival analysis with interpretable coefficients
CoxPHSurvivalAnalysis: Basic Cox modelCoxnetSurvivalAnalysis: Penalized Cox with elastic net for high-dimensional dataIPCRidge: Ridge regression for accelerated failure time models
See: references/cox-models.md for detailed guidance on Cox models, regularization, and interpretation
Ensemble Methods
Use for: High predictive performance with complex non-linear relationships
RandomSurvivalForest: Robust, non-parametric ensemble methodGradientBoostingSurvivalAnalysis: Tree-based boosting for maximum performanceComponentwiseGradientBoostingSurvivalAnalysis: Linear boosting with feature selectionExtraSurvivalTrees: Extremely randomized trees for additional regularization
See: references/ensemble-models.md for comprehensive guidance on ensemble methods, hyperparameter tuning, and when to use each model
Survival Support Vector Machines
Use for: Medium-sized datasets with margin-based learning
FastSurvivalSVM: Linear SVM optimized for speedFastKernelSurvivalSVM: Kernel SVM for non-linear relationshipsHingeLossSurvivalSVM: SVM with hinge lossClinicalKernelTransform: Specialized kernel for clinical + molecular data
See: references/svm-models.md for detailed SVM guidance, kernel selection, and hyperparameter tuning
Model Selection Decision Tree
Start
├─ High-dimensional data (p > n)?
│ ├─ Yes → CoxnetSurvivalAnalysis (elastic net)
│ └─ No → Continue
│
├─ Need interpretable coefficients?
│ ├─ Yes → CoxPHSurvivalAnalysis or ComponentwiseGradientBoostingSurvivalAnalysis
│ └─ No → Continue
│
├─ Complex non-linear relationships expected?
│ ├─ Yes
│ │ ├─ Large dataset (n > 1000) → GradientBoostingSurvivalAnalysis
│ │ ├─ Medium dataset → RandomSurvivalForest or FastKernelSurvivalSVM
│ │ └─ Small dataset → RandomSurvivalForest
│ └─ No → CoxPHSurvivalAnalysis or FastSurvivalSVM
│
└─ For maximum performance → Try multiple models and compare
2. Data Preparation and Preprocessing
Before modeling, properly prepare survival data:
Creating Survival Outcomes
from sksurv.util import Surv
y = Surv.from_arrays(event=event_array, time=time_array)
y = Surv.from_dataframe('event', 'time', df)
Essential Preprocessing Steps
- Handle missing values: Imputation strategies for features
- Encode categorical variables: One-hot encoding or label encoding
- Standardize features: Critical for SVMs and regularized Cox models
- Validate data quality: Check for negative times, sufficient events per feature
- Train-test split: Maintain similar censoring rates across splits
See: references/data-handling.md for complete preprocessing workflows, data validation, and best practices
3. Model Evaluation
Proper evaluation is critical for survival models. Use appropriate metrics that account for censoring:
Concordance Index (C-index)
Primary metric for ranking/discrimination:
- Harrell's C-index: Use for low censoring (<40%)
- Uno's C-index: Use for moderate to high censoring (>40%) - more robust
from sksurv.metrics import concordance_index_censored, concordance_index_ipcw
c_harrell = concordance_index_censored(y_test['event'], y_test['time'], risk_scores)[0]
c_uno = concordance_index_ipcw(y_train, y_test, risk_scores)[0]
Time-Dependent AUC
Evaluate discrimination at specific time points:
from sksurv.metrics import cumulative_dynamic_auc
times = [365, 730, 1095]
auc, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk_scores, times)
Brier Score
Assess both discrimination and calibration:
from sksurv.metrics import integrated_brier_score
ibs = integrated_brier_score(y_train, y_test, survival_functions, times)
See: references/evaluation-metrics.md for comprehensive evaluation guidance, metric selection, and using scorers with cross-validation
4. Competing Risks Analysis
Handle situations with multiple mutually exclusive event types:
from sksurv.nonparametric import cumulative_incidence_competing_risks
time_points, cif_event1, cif_event2 = cumulative_incidence_competing_risks(y)
Use competing risks when:
- Multiple mutually exclusive event types exist (e.g., death from different causes)
- Occurrence of one event prevents others
- Need probability estimates for specific event types
See: references/competing-risks.md for detailed competing risks methods, cause-specific hazard models, and interpretation
5. Non-parametric Estimation
Estimate survival functions without parametric assumptions:
Kaplan-Meier Estimator
from sksurv.nonparametric import kaplan_meier_estimator
time, survival_prob = kaplan_meier_estimator(y['event'], y['time'])
Nelson-Aalen Estimator
from sksurv.nonparametric import nelson_aalen_estimator
time, cumulative_hazard = nelson_aalen_estimator(y['event'], y['time'])
Typical Workflows
Workflow 1: Standard Survival Analysis
from sksurv.datasets import load_breast_cancer
from sksurv.linear_model import CoxPHSurvivalAnalysis
from sksurv.metrics import concordance_index_ipcw
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
X, y = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
estimator = CoxPHSurvivalAnalysis()
estimator.fit(X_train_scaled, y_train)
risk_scores = estimator.predict(X_test_scaled)
c_index = concordance_index_ipcw(y_train, y_test, risk_scores)[0]
print(f"C-index: {c_index:.3f}")
Workflow 2: High-Dimensional Data with Feature Selection
from sksurv.linear_model import CoxnetSurvivalAnalysis
from sklearn.model_selection import GridSearchCV
from sksurv.metrics import as_concordance_index_ipcw_scorer
estimator = CoxnetSurvivalAnalysis(l1_ratio=0.9)
param_grid = {'alpha_min_ratio': [0.01, 0.001]}
cv = GridSearchCV(estimator, param_grid,
scoring=as_concordance_index_ipcw_scorer(), cv=5)
cv.fit(X, y)
best_model = cv.best_estimator_
selected_features = np.where(best_model.coef_ != 0)[0]
Workflow 3: Ensemble Method for Maximum Performance
from sksurv.ensemble import GradientBoostingSurvivalAnalysis
from sklearn.model_selection import GridSearchCV
param_grid = {
'learning_rate': [0.01, 0.05, 0.1],
'n_estimators': [100, 200, 300],
'max_depth': [3, 5, 7]
}
gbs = GradientBoostingSurvivalAnalysis()
cv = GridSearchCV(gbs, param_grid, cv=5,
scoring=as_concordance_index_ipcw_scorer(), n_jobs=-1)
cv.fit(X_train, y_train)
best_model = cv.best_estimator_
risk_scores = best_model.predict(X_test)
c_index = concordance_index_ipcw(y_train, y_test, risk_scores)[0]
Workflow 4: Comprehensive Model Comparison
from sksurv.linear_model import CoxPHSurvivalAnalysis
from sksurv.ensemble import RandomSurvivalForest, GradientBoostingSurvivalAnalysis
from sksurv.svm import FastSurvivalSVM
from sksurv.metrics import concordance_index_ipcw, integrated_brier_score
models = {
'Cox': CoxPHSurvivalAnalysis(),
'RSF': RandomSurvivalForest(n_estimators=100, random_state=42),
'GBS': GradientBoostingSurvivalAnalysis(random_state=42),
'SVM': FastSurvivalSVM(random_state=42)
}
results = {}
for name, model in models.items():
model.fit(X_train_scaled, y_train)
risk_scores = model.predict(X_test_scaled)
c_index = concordance_index_ipcw(y_train, y_test, risk_scores)[0]
results[name] = c_index
print(f"{name}: C-index = {c_index:.3f}")
best_model_name = max(results, key=results.get)
print(f"\nBest model: {best_model_name}")
Integration with scikit-learn
scikit-survival fully integrates with scikit-learn's ecosystem:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score, GridSearchCV
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', CoxPHSurvivalAnalysis())
])
scores = cross_val_score(pipeline, X, y, cv=5,
scoring=as_concordance_index_ipcw_scorer())
param_grid = {'model__alpha': [0.1, 1.0, 10.0]}
cv = GridSearchCV(pipeline, param_grid, cv=5)
cv.fit(X, y)
Best Practices
- Always standardize features for SVMs and regularized Cox models
- Use Uno's C-index instead of Harrell's when censoring > 40%
- Report multiple evaluation metrics (C-index, integrated Brier score, time-dependent AUC)
- Check proportional hazards assumption for Cox models
- Use cross-validation for hyperparameter tuning with appropriate scorers
- Validate data quality before modeling (check for negative times, sufficient events per feature)
- Compare multiple model types to find best performance
- Use permutation importance for Random Survival Forests (not built-in importance)
- Consider competing risks when multiple event types exist
- Document censoring mechanism and rates in analysis
Common Pitfalls to Avoid
- Using Harrell's C-index with high censoring → Use Uno's C-index
- Not standardizing features for SVMs → Always standardize
- Forgetting to pass y_train to concordance_index_ipcw → Required for IPCW calculation
- Treating competing events as censored → Use competing risks methods
- Not checking for sufficient events per feature → Rule of thumb: 10+ events per feature
- Using built-in feature importance for RSF → Use permutation importance
- Ignoring proportional hazards assumption → Validate or use alternative models
- Not using appropriate scorers in cross-validation → Use as_concordance_index_ipcw_scorer()
Reference Files
This skill includes detailed reference files for specific topics:
references/cox-models.md: Complete guide to Cox proportional hazards models, penalized Cox (CoxNet), IPCRidge, regularization strategies, and interpretationreferences/ensemble-models.md: Random Survival Forests, Gradient Boosting, hyperparameter tuning, feature importance, and model selectionreferences/evaluation-metrics.md: Concordance index (Harrell's vs Uno's), time-dependent AUC, Brier score, comprehensive evaluation pipelinesreferences/data-handling.md: Data loading, preprocessing workflows, handling missing data, feature encoding, validation checksreferences/svm-models.md: Survival Support Vector Machines, kernel selection, clinical kernel transform, hyperparameter tuningreferences/competing-risks.md: Competing risks analysis, cumulative incidence functions, cause-specific hazard models
Load these reference files when detailed information is needed for specific tasks.
Additional Resources
Quick Reference: Key Imports
from sksurv.linear_model import CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis, IPCRidge
from sksurv.ensemble import RandomSurvivalForest, GradientBoostingSurvivalAnalysis
from sksurv.svm import FastSurvivalSVM, FastKernelSurvivalSVM
from sksurv.tree import SurvivalTree
from sksurv.metrics import (
concordance_index_censored,
concordance_index_ipcw,
cumulative_dynamic_auc,
brier_score,
integrated_brier_score,
as_concordance_index_ipcw_scorer,
as_integrated_brier_score_scorer
)
from sksurv.nonparametric import (
kaplan_meier_estimator,
nelson_aalen_estimator,
cumulative_incidence_competing_risks
)
from sksurv.util import Surv
from sksurv.preprocessing import OneHotEncoder, encode_categorical
from sksurv.datasets import load_gbsg2, load_breast_cancer, load_veterans_lung_cancer
from sksurv.kernels import ClinicalKernelTransform
