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scikit-survival

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A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks.

scientific-skills/Data Analysis/scikit-survival/SKILL.md

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Sep 17, 2026
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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

Use this skill when you need to:

1. Model **time-to-event outcomes with censoring** (right/left/interval censored observations).
2. Fit and interpret **Cox Proportional Hazards** models (including **penalized** Cox for high-dimensional data).
3. Train **non-linear survival models** such as **Random Survival Forests** or **Gradient Boosting** survival models.
4. Use **Survival SVMs** for margin-based survival prediction (linear or kernel).
5. Evaluate survival predictions with **censoring-aware metrics** (Uno/Harrell C-index, time-dependent AUC, Brier/Integrated Brier Score) and/or perform **competing risks** analysis.

## Key Features

- **Survival target construction** via `sksurv.util.Surv` (arrays or DataFrame).
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