alterlab-scikit-survival
CommunitySurvival analysis toolkit for Python
Education & Research#scikit-survival#survival-analysis#time-to-event#competing-risks#risk-prediction#cox-models
AuthorAlterLab-IEU
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Part of the AlterLab Academic Skills suite, this skill provides a comprehensive workflow for survival analysis in Python using scikit-survival, enabling researchers to work with censored data, time-to-event modeling, competing risks, and robust evaluation.
Core Features & Use Cases
- Supports Cox proportional hazards models (CoxPHSurvivalAnalysis), penalized Cox (CoxnetSurvivalAnalysis), and IPCRidge.
- Includes ensemble methods (RandomSurvivalForest, GradientBoostingSurvivalAnalysis, ExtraSurvivalTrees) and Survival SVMs (FastSurvivalSVM, FastKernelSurvivalSVM, HingeLossSurvivalSVM) for flexible modeling.
- Facilitates data preprocessing, survival outcome creation with Surv, and competing risks analysis with CIFs, plus evaluation with concordance index and Brier score.
- Real-world use: analyze censored studies, compare models, and predict risk or survival probabilities.
Quick Start
Load your dataset, prepare a Surv object, then fit a CoxPHSurvivalAnalysis model to obtain risk scores.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: alterlab-scikit-survival Download link: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/archive/main.zip#alterlab-scikit-survival Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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