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scikit-survival

Fit Cox proportional hazards models and other survival analysis methods with scikit-survival.

Updated May 8, 2026
Search Tags:#survival analysis#svm#cox models#ensemble methods#time-to-event analysis
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scikit-survival-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/clinical-biomedical-ai/scikit-survival
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scikit-survival-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-survival, scikit-learn, numpy, pandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses complex survival analysis and time-to-event modeling challenges, leveraging the scikit-survival library for accurate predictions and insights.

Core Features & Use Cases

  • Survival Analysis: Perform survival analysis and time-to-event modeling with robust models.
  • Cox Models: Fit and interpret Cox proportional hazards models for standard survival analysis.
  • Ensemble Methods: Utilize Random Survival Forests, Gradient Boosting, and other ensemble methods for high predictive performance.
  • SVMs: Train and evaluate Survival Support Vector Machines for non-linear relationships.
  • Use Case: Imagine you are a medical researcher analyzing patient data and want to predict the time until a patient's event of interest (e.g., relapse, death) occurs.

Quick Start

Activate the scikit-survival skill and use the 'coxph' model to fit a survival analysis on the provided dataset 'patient_data.csv'.

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 with scikit-learn and pandas?▼

Survival analysis can be performed using the scikit-survival library, which integrates with scikit-learn and pandas to fit time-to-event models on datasets like patient_data.csv.

What's the best way to model time-to-event data for clinical trials?▼

Time-to-event data for clinical trials is best modeled using Cox proportional hazards, ensemble methods like Random Survival Forests, or Survival SVMs provided by the scikit-survival library.

Can I use Random Survival Forests and Gradient Boosting for time-to-event analysis?▼

Random Survival Forests and Gradient Boosting are available as ensemble methods for time-to-event analysis, offering high predictive performance for complex survival modeling.

How do I fit a Cox proportional hazards model on patient data?▼

Fitting a Cox proportional hazards model involves activating the scikit-survival skill and using the 'coxph' model to process your patient data for standard survival analysis and predictions.

Do I need scikit-survival installed to train Survival Support Vector Machines?▼

Yes, scikit-survival is required as a dependency alongside scikit-learn and numpy to train and evaluate Survival Support Vector Machines for non-linear relationships in time-to-event data.

When should I use Survival SVMs instead of Cox models for medical research?▼

Survival SVMs should be used instead of Cox models when your medical research data involves non-linear relationships, whereas Cox models suit standard proportional hazards survival analysis.

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