/scikit-survival

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.

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

1. **Handle missing values**: Imputation strategies for features 2. **Encode categorical variables**: One-hot encoding or label encoding 3. **Standardize features**: Critical for SVMs and regularized Cox models 4. **Validate data quality**: Check for negative times, sufficient events per feature 5. **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