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scikit-survival

Fit survival models and evaluate with C-index, AUC, and Brier score.

Updated Mar 10, 2026
Search Tags:#machine learning#statistics#survival analysis#cox model#time-to-event#censored data
One-click install
npx skills add https://github.com/Yezez9/Research-Agent --skill scikit-survival-yezez9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/scikit-survival
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill scikit-survival-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill empowers users to perform sophisticated survival analysis and time-to-event modeling, especially when dealing with censored data, a common challenge in medical research and beyond.

Core Features & Use Cases

  • Model Diverse Survival Data: Fit Cox proportional hazards models, Random Survival Forests, Gradient Boosting models, and Survival SVMs.
  • Handle Censoring: Accurately analyze data where the event of interest hasn't occurred for all subjects.
  • Evaluate Performance: Utilize metrics like concordance index (C-index), time-dependent AUC, and Brier score for robust model assessment.
  • Use Case: Analyze patient data to predict time to disease recurrence, accounting for patients who are still in remission at the end of the study.

Quick Start

Use the scikit-survival skill to fit a Cox proportional hazards model to the provided survival data.

Frequently Asked Questions about scikit-survival

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I model time-to-event data with censored observations in Python?▼

Survival analysis models time-to-event data by fitting algorithms like Cox proportional hazards to handle censored observations where the event hasn't occurred. This Skill provides preprocessing and fitting capabilities to accurately predict event times.

What is the concordance index and how is it used to evaluate survival models?▼

The concordance index (C-index) evaluates survival models by measuring ranking capability for predicted survival times. This Skill computes C-index alongside time-dependent AUC and Brier score to assess model discrimination and calibration.

Can I use Random Survival Forests and Gradient Boosting for censored data?▼

Random Survival Forests and Gradient Boosting can model censored data by adapting tree-based ensemble methods for time-to-event scenarios. This Skill fits these models alongside Survival SVMs to handle complex nonlinear relationships.

What's the best way to fit a Cox proportional hazards model in Python?▼

Fitting a Cox proportional hazards model in Python involves preparing censored data and applying the algorithm to estimate hazard ratios. This Skill implements CoxPH and CoxNet models, enabling robust feature engineering and risk prediction.

How does survival analysis handle competing risks in time-to-event modeling?▼

Survival analysis handles competing risks by modeling time-to-event data where multiple distinct terminal events can occur. This Skill supports competing risks scenarios alongside standard censored data analysis for comprehensive event prediction.

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