scikit-survival

Survival analysis on right-censored time-to-event data in Python is performed using Cox proportional hazards models, survival SVMs, or ensemble methods like Random Survival Forest. These estimators handle censoring-aware fitting to predict event risks over time.

You can evaluate survival model performance using time-dependent AUC, Brier score, Harrell's C-index, and Uno's C-index. These metrics account for censoring-aware evaluation to provide accurate discrimination and calibration measurements for time-to-event predictions.

Survival analysis models integrate seamlessly with scikit-learn pipelines for data preprocessing. This allows you to combine scaling, imputation, and feature selection steps directly with survival estimators like CoxPHSurvivalAnalysis or RandomSurvivalForest in a unified workflow.

The best way to handle high-dimensional data in survival analysis is using penalized Cox models like CoxnetSurvivalAnalysis. This approach applies L1 or L2 penalties to perform feature selection while fitting the survival model, preventing overfitting on datasets with many features.

Competing risks occur when multiple distinct event types prevent the primary event of interest from happening, and Python can model them using non-parametric estimations. The survival analysis toolkit provides specialized methods to estimate cumulative incidence functions in the presence of competing risks.