scikit-survival
scikit-survival is a Python library for survival analysis that handles censored data in time-to-event modeling. It offers Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, and Survival SVMs, with evaluation metrics including concordance index and Brier score. Use it for competing risks analysis, Kaplan-Meier estimation, and any survival analysis workflow.
scikit-survival lets you build and evaluate survival models that handle censored time-to-event data using Cox, ensemble, and SVM approaches.
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Decision gist · record as of 2026-07-27
scikit-survival lets you build and evaluate survival models that handle censored time-to-event data using Cox, ensemble, and SVM approaches. scikit-survival is a Python library for survival analysis that handles censored data in time-to-event modeling. It offers Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, and Survival SVMs, with evaluation metrics including concordance index and Brier score. Use it for competing risks analysis, Kaplan-Meier estimation, and any survival analysis workflow.
Use it when
- scikit-survival provides specialized preprocessing and validation tools for survival datasets with proper censoring handling.
- scikit-survival enables comparison of multiple survival model types including Cox proportional hazards regression, Random Survival Forests.
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synthetic-sciences/openscience/scikit-survival · repository language: TypeScript
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is scikit-survival used for in survival analysis?
scikit-survival is a Python library for survival analysis that handles censored data in time-to-event modeling. It offers Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, and Survival SVMs, with evaluation metrics including concordance index and Brier score. Use it for competing risks analysis, Kaplan-Meier estimation, and any survival analysis workflow.
How does scikit-survival handle censored data analysis?
scikit-survival provides specialized preprocessing and validation tools for survival datasets with proper censoring handling. It supports right-censored data structures and integrates censoring information directly into model training and evaluation, ensuring accurate time-to-event predictions even when some subjects' event times are unknown or incomplete.
What survival model types can scikit-survival compare?
scikit-survival enables comparison of multiple survival model types including Cox proportional hazards regression, Random Survival Forests, Gradient Boosting survival models, and Survival SVMs. You can evaluate each model's performance to identify the best approach for your specific time-to-event prediction task.
Which evaluation metrics does scikit-survival calculate?
scikit-survival calculates key survival metrics: concordance index (C-index) for ranking accuracy, Brier score for prediction error, and time-dependent AUC for assessing discrimination at specific time points. These metrics enable rigorous evaluation of survival model performance on censored data.
Can scikit-survival estimate non-parametric survival curves?
Yes, scikit-survival supports non-parametric survival curve estimation through Kaplan-Meier and Nelson-Aalen estimators. It also handles competing risks analysis, allowing you to model scenarios where multiple distinct event types can occur and may prevent observation of other events.
How do I get started with scikit-survival for time to event modeling?
scikit-survival provides tutorials and documentation for time-to-event modeling workflows. Start by preprocessing your censored dataset, choose a model type (Cox, ensemble, or SVM), train it on your data, then evaluate using concordance index or Brier score. The library integrates with scikit-learn conventions for familiar usage patterns.
SKILL.md
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scikit-survival: Survival Analysis in Python
Overview
scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.
Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods).
When to Use This Skill
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backend/cli/skills/biology/scikit-survival/SKILL.md
backend/cli/skills/biology/scikit-survival/references/competing-risks.md
backend/cli/skills/biology/scikit-survival/references/cox-models.md
backend/cli/skills/biology/scikit-survival/references/data-handling.md
backend/cli/skills/biology/scikit-survival/references/ensemble-models.md
backend/cli/skills/biology/scikit-survival/references/evaluation-metrics.md
backend/cli/skills/biology/scikit-survival/references/svm-models.md
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Related skills
Scikit Survival Analysis extends scikit-learn for right-censored survival data, offering Cox proportional hazards (including elastic net variants), ensemble methods like Random Survival Forests and Gradient Boosting, and survival SVMs. It provides censoring-aware evaluation metrics and non-parametric survival curve estimation.
scikit-survival 0.28.0 enables you to construct, tune, and assess survival workflows for right-censored outcomes using Cox models, ensemble methods, and kernel-based approaches. The skill enforces leakage-safe data splits, preprocessing pipelines, and proper metric selection—discrimination, calibration, and prediction error—while handling competing risks through nonparametric cumulative incidence.
This skill handles survival analysis and time-to-event data common in clinical research, including Kaplan-Meier estimation, Cox proportional hazards regression, and competing risks modeling. It also covers longitudinal data analysis, diagnostic accuracy assessment, and missing data strategies tailored to biomedical studies.
This skill teaches statistical methods for analyzing when events occur, accounting for incomplete observations where some subjects haven't experienced the event yet. You'll learn Kaplan-Meier estimation for survival curves, Cox regression for modeling risk factors, and log-rank tests for comparing groups—with practical Python implementations using lifelines.
Lifelines is a Python survival analysis library built for medical research and epidemiology. It models time-to-event outcomes like disease progression or recovery, properly accounting for censored data from patients who leave studies early. Use it to estimate survival curves, compare treatment groups, and identify risk factors through Cox proportional hazards regression.
scikit-learn is Python's go-to library for classical machine learning across classification, regression, clustering, and dimensionality reduction. It includes algorithms ranging from linear models and decision trees to ensemble methods and support vector machines, plus comprehensive tools for preprocessing, model evaluation, and hyperparameter tuning. Use it to build production-ready ML pipelines with built-in cross-validation and feature engineering capabilities.