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scikit-survival

Perform survival analysis on censored time-to-event data with scikit-survival.

Updated Aug 23, 2026
Search Tags:#analysis#scikit-survival#survival#time-to-event#competing-risks#cox#censored-data
One-click install
npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill scikit-survival-bkzhao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/BKZhao/MIMIC-Papers-Repro-Agent/tree/main/.codex/skills/scikit-survival
Command: npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill scikit-survival-bkzhao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scikit-survival provides a comprehensive Python toolkit for survival analysis on censored time-to-event data, enabling model fitting, interpretation, and evaluation with estimators such as CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis, IPCRidge, RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and various survival SVMs, along with robust metrics for performance assessment.

Core Features & Use Cases

  • Supports multiple model families (CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis, IPCRidge, RSF, GradientBoostingSurvivalAnalysis, ComponentwiseGradientBoostingSurvivalAnalysis, FastSurvivalSVM, FastKernelSurvivalSVM) for diverse survival analyses.
  • Provides data handling, preprocessing, and evaluation workflows for censored data, including Kaplan-Meier/Nelson-Aalen estimates and competing risks support.
  • Suitable for clinical and observational research requiring rigorous end-to-end survival analysis pipelines.

Quick Start

Load a survival dataset and fit a CoxPHSurvivalAnalysis to generate hazard ratios and risk scores.

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 time-to-event data in Python?▼

Survival analysis on censored time-to-event data in Python can be performed using scikit-survival to fit models like CoxPHSurvivalAnalysis, generating hazard ratios, risk scores, and evaluation metrics for clinical and observational research.

What is the best way to model competing risks for survival data?▼

Modeling competing risks for survival data is handled within scikit-survival through specialized estimators and data handling workflows, supporting rigorous end-to-end analysis pipelines for clinical studies requiring competing risks analysis.

Can I use Random Survival Forest and Gradient Boosting for hazard modeling?▼

Yes, you can use RandomSurvivalForest and GradientBoostingSurvivalAnalysis for hazard modeling along with CoxnetSurvivalAnalysis, IPCRidge, and various survival SVMs to model censored time-to-event data.

How do I generate risk scores and hazard ratios from clinical study data?▼

To generate risk scores and hazard ratios from clinical study data, load your Surv-like data input into scikit-survival and fit a CoxPHSurvivalAnalysis model, which outputs the required hazard ratios and risk scores.

Does scikit-survival support Kaplan-Meier estimates and survival SVMs?▼

Yes, scikit-survival supports Kaplan-Meier and Nelson-Aalen estimates for data preprocessing, alongside FastSurvivalSVM and FastKernelSurvivalSVM for fitting survival models on censored time-to-event data.

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