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scikit-survival

Fit Cox models and survival analysis pipelines with scikit-learn integration.

8|Updated Nov 19, 2025
Search Tags:#machine learning#statistics#survival analysis#cox model#time-to-event#censored data
One-click install
npx skills add https://github.com/sanand0/scientific-research --skill scikit-survival-sanand0
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/scikit-survival
Command: npx skills add https://github.com/sanand0/scientific-research --skill scikit-survival-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for performing survival analysis and time-to-event modeling in Python, enabling users to analyze censored data and understand event timings.

Core Features & Use Cases

  • Model Fitting: Supports Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs.
  • Data Handling: Includes utilities for creating survival outcomes and preprocessing data.
  • Evaluation: Offers metrics like concordance index, time-dependent AUC, and Brier score.
  • Use Case: Analyze patient survival data to understand the impact of treatments and patient characteristics on time to recovery or death, accounting for patients who are still alive at the end of the study.

Quick Start

Use the scikit-survival skill to fit a Cox proportional hazards model to the provided breast cancer dataset.

Frequently Asked Questions about scikit-survival

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

FAQPage Schema
How do I perform survival analysis on censored data in Python?▼

You can perform survival analysis on censored data in Python by fitting models like Cox proportional hazards, Random Survival Forests, and Survival SVMs to analyze time-to-event timings.

What is the best way to evaluate a Cox model with time-to-event data?▼

The best way to evaluate a Cox model with time-to-event data is using robust metrics like the concordance index, time-dependent AUC, and Brier score to measure prediction accuracy on censored outcomes.

Can I use scikit-learn pipelines and cross-validation with survival models?▼

Yes, you can use scikit-learn integration for pipelines and cross-validation with survival models, allowing you to streamline data preprocessing and model fitting for censored data analysis.

Does Python support ensemble methods for time-to-event modeling?▼

Python supports ensemble methods for time-to-event modeling through Gradient Boosting and Random Survival Forests, enabling robust predictions on censored data alongside traditional Survival SVMs.

How do I handle preprocessing for survival analysis with censored data?▼

Handle preprocessing for survival analysis with censored data by using included utilities to create survival outcomes and preprocess datasets before fitting models like Cox proportional hazards or Survival SVMs.

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