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scikit-survival

Fit Cox proportional hazards models on censored survival datasets.

2|Updated Jun 4, 2026
Search Tags:#machine learning#survival analysis#scikit-learn#cox model#time-to-event#censored data
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill scikit-survival-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/scikit-survival
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill scikit-survival-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of analyzing time-to-event data where observations are censored, providing a robust framework for modeling survival outcomes that standard regression techniques cannot handle.

Core Features & Use Cases

  • Survival Modeling: Implement Cox proportional hazards, Random Survival Forests, and Gradient Boosting models to predict event times.
  • Performance Evaluation: Utilize specialized metrics like Uno's C-index, time-dependent AUC, and Brier score to assess model accuracy under censoring.
  • Use Case: A researcher can use this skill to analyze clinical trial data to determine the impact of specific treatments on patient survival while accounting for patients who dropped out of the study before the event occurred.

Quick Start

Use the scikit-survival skill to fit a Cox proportional hazards model on the provided survival dataset and evaluate its performance using the concordance index.

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 using scikit-learn?▼

Survival analysis on right-censored data uses scikit-survival to fit models like the Cox proportional hazards model, enabling hazard estimation and risk scoring for time-to-event outcomes within the scikit-learn ecosystem.

What is the best way to model time-to-event data with right-censored observations?▼

Time-to-event modeling for right-censored data is handled by scikit-survival, which provides Random Survival Forests and Gradient Boosting to accurately predict event times when standard regression techniques fail.

Can I use Random Survival Forests for clinical trial data with dropout patients?▼

Random Survival Forests can analyze clinical trial data by accounting for right-censored observations from dropout patients, providing non-parametric survival function estimation to determine treatment impact on patient survival.

How do you evaluate survival model accuracy under censoring?▼

Survival model accuracy under censoring is evaluated using specialized metrics like Uno's C-index, time-dependent AUC, and Brier score, which correctly assess performance when standard validation methods are insufficient.

Does scikit-survival support competing risks in reliability engineering tasks?▼

Scikit-survival supports competing risks and right-censored datasets, allowing reliability engineering tasks to perform hazard estimation and risk scoring for time-to-event failures using scikit-learn compatible models.

When should I not use standard regression for time-to-event data?▼

Standard regression should not be used for time-to-event data when observations are censored, as it cannot handle right-censored data or competing risks, requiring scikit-survival models for accurate hazard estimation instead.

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