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scikit-survival

基于scikit-survival的Python生存分析与事件时间建模综合工具包。适用于处理删失生存数据、执行事件时间分析、拟合Cox比例风险模型、随机生存森林、梯度提升模型或生存支持向量机，使用一致性指数或Brier评分评估生存预测，处理竞争风险，以及通过scikit-survival库实现任何生存分析工作流程。

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name:scikit-survivaldescription: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 licensemetadata: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 model

  • CoxnetSurvivalAnalysis: Penalized Cox with elastic net for high-dimensional data

  • IPCRidge: 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 method

  • GradientBoostingSurvivalAnalysis: Tree-based boosting for maximum performance

  • ComponentwiseGradientBoostingSurvivalAnalysis: Linear boosting with feature selection

  • ExtraSurvivalTrees: 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 speed

  • FastKernelSurvivalSVM: Kernel SVM for non-linear relationships

  • HingeLossSurvivalSVM: SVM with hinge loss

  • ClinicalKernelTransform: 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
    From separate arrays

    y = Surv.from_arrays(event=event_array, time=time_array)
    From DataFrame

    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
    Harrell's C-index

    c_harrell = concordance_index_censored(y_test['event'], y_test['time'], risk_scores)[0]
    Uno's C-index (recommended)

    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]  # 1, 2, 3 years
    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
    Estimate cumulative incidence for each event type

    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
    1. Load and prepare data

    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)
    2. Preprocess

    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    3. Fit model

    estimator = CoxPHSurvivalAnalysis()
    estimator.fit(X_train_scaled, y_train)
    4. Predict

    risk_scores = estimator.predict(X_test_scaled)
    5. Evaluate

    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
    1. Use penalized Cox for feature selection

    estimator = CoxnetSurvivalAnalysis(l1_ratio=0.9)  # Lasso-like
    2. Tune regularization with cross-validation

    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)
    3. Identify selected features

    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
    1. Define parameter grid

    param_grid = {
        'learning_rate': [0.01, 0.05, 0.1],
        'n_estimators': [100, 200, 300],
        'max_depth': [3, 5, 7]
    }
    2. Grid search

    gbs = GradientBoostingSurvivalAnalysis()
    cv = GridSearchCV(gbs, param_grid, cv=5,
                      scoring=as_concordance_index_ipcw_scorer(), n_jobs=-1)
    cv.fit(X_train, y_train)
    3. Evaluate best model

    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
    Define models

    models = {
        'Cox': CoxPHSurvivalAnalysis(),
        'RSF': RandomSurvivalForest(n_estimators=100, random_state=42),
        'GBS': GradientBoostingSurvivalAnalysis(random_state=42),
        'SVM': FastSurvivalSVM(random_state=42)
    }
    Evaluate each model

    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}")
    Select best model

    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
    Use pipelines

    pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('model', CoxPHSurvivalAnalysis())
    ])
    Use cross-validation

    scores = cross_val_score(pipeline, X, y, cv=5,
                             scoring=as_concordance_index_ipcw_scorer())
    Use grid search

    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 interpretation

  • references/ensemble-models.md: Random Survival Forests, Gradient Boosting, hyperparameter tuning, feature importance, and model selection

  • references/evaluation-metrics.md: Concordance index (Harrell's vs Uno's), time-dependent AUC, Brier score, comprehensive evaluation pipelines

  • references/data-handling.md: Data loading, preprocessing workflows, handling missing data, feature encoding, validation checks

  • references/svm-models.md: Survival Support Vector Machines, kernel selection, clinical kernel transform, hyperparameter tuning

  • references/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

  • Official Documentation: https://scikit-survival.readthedocs.io/

  • GitHub Repository: https://github.com/sebp/scikit-survival

  • Built-in Datasets: Use sksurv.datasets for practice datasets (GBSG2, WHAS500, veterans lung cancer, etc.)

  • API Reference: Complete list of classes and functions at https://scikit-survival.readthedocs.io/en/stable/api/index.html

    • Quick Reference: Key Imports

    # Models
    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
    Evaluation metrics

    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
    )
    Non-parametric estimation

    from sksurv.nonparametric import (
        kaplan_meier_estimator,
        nelson_aalen_estimator,
        cumulative_incidence_competing_risks
    )
    Data handling

    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
    Kernels

    from sksurv.kernels import ClinicalKernelTransform

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