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scikit-survival

Fit survival models and evaluate time-to-event predictions with scikit-survival.

557|98|Updated Nov 7, 2025
Search Tags:#machine learning#statistics#survival analysis#cox model#time-to-event#censored data
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill scikit-survival-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/scikit-survival
Command: npx skills add https://github.com/jimmc414/Kosmos --skill scikit-survival-jimmc414

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 Fitting: Supports Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs.
  • Data Handling: Provides tools for creating survival outcomes, preprocessing data, and handling competing risks.
  • Evaluation: Offers 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 event-free at the end of the study (censoring).

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 handle censored data in survival analysis with Python?▼

Survival analysis with Python handles censored data by creating survival-specific outcome variables that track both time-to-event durations and event occurrence indicators, allowing models to process incomplete observations accurately.

What models can I fit for time-to-event modeling besides the Cox proportional hazards model?▼

Time-to-event modeling supports fitting Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs, providing multiple machine learning approaches for analyzing censored data.

How do I evaluate survival model performance using metrics like C-index and Brier score?▼

Evaluate survival model performance using the Concordance Index (C-index), time-dependent AUC, and Brier Score, which measure ranking quality and prediction accuracy for censored time-to-event data.

When do I need survival analysis instead of standard regression for my data?▼

Survival analysis is needed when modeling time-to-event data with censoring, such as predicting disease recurrence time for patients who remain event-free at study end, where standard regression cannot handle incomplete follow-up periods.

Can I use scikit-survival to handle competing risks in time-to-event data?▼

Yes, survival analysis with scikit-survival provides tools for handling competing risks alongside data preprocessing, allowing you to model scenarios where multiple distinct event types can occur.

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