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scikit-survival

Perform survival analysis and competing risks with scikit-survival.

Updated Dec 17, 2025
Search Tags:#survival analysis#time-to-event#svm#cox proportional hazards#competing risks#ensembles
One-click install
npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill scikit-survival-robotlearning123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/robotlearning123/claude-scientific-skills/tree/main/scientific-skills/scikit-survival
Command: npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill scikit-survival-robotlearning123

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill unit empowers you to perform robust survival analysis and time-to-event modeling directly in Python, addressing challenges like censored data, complex models, and competitive risks.

Core Features & Use Cases

  • Survival Analysis: Handle right-censored data with specialized survival models, including Cox, ensemble, and SVM-based methods.
  • Competing Risks: Integrate models like Fine-Gray for accurate competing risk analysis.
  • Data Preprocessing: Preprocess data, handle missing values, and select important features for modeling.
  • Evaluation Metrics: Use metrics like concordance index and Brier score for thorough performance evaluation.
  • Use Case: Analyze survival times from clinical trial data to identify predictive biomarkers or correlate outcomes with treatment types.

Quick Start

Activate the scikit-survival skill with: from sksurv.linear_model import CoxPHSurvivalAnalysis then use it as a classifier: estimator = CoxPHSurvivalAnalysis()

Frequently Asked Questions about scikit-survival

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

FAQPage Schema
How do I handle right-censored time-to-event data in Python for survival analysis?▼

Survival analysis of right-censored time-to-event data in Python requires specialized models like Cox proportional hazards and ensemble methods to process clinical trial data and identify predictive biomarkers. This Skill provides the scikit-survival wrappers to handle and model this data.

Can I model competing risks using scikit-survival alongside standard survival models?▼

Yes, competing risks analysis can be modeled using scikit-survival by integrating Fine-Gray models to accurately account for multiple concurrent events, complementing standard Cox proportional hazards and SVM-based survival methods for comprehensive time-to-event modeling.

What evaluation metrics are available for survival analysis models in scikit-survival?▼

Survival analysis model evaluation in scikit-survival supports the concordance index and Brier score to thoroughly measure prediction performance on time-to-event data, ensuring accurate handling of censored clinical trial outcomes.

Does this approach work with pandas and scikit-learn for clinical trial data preprocessing?▼

Yes, this survival analysis approach works with pandas and scikit-learn to preprocess data, handle missing values, and select important features before fitting time-to-event models like CoxPHSurvivalAnalysis to clinical trial data.

What is the best way to analyze treatment outcomes and predictive biomarkers with time-to-event data?▼

The best way to analyze treatment outcomes with time-to-event data is using survival analysis with SVM-based methods and ensembles to correlate outcomes with treatment types and identify predictive biomarkers while properly handling right-censored observations.

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