scikit-survival

Survival analysis on censored data in Python uses models like Cox proportional hazards and ensemble methods to estimate time-to-event outcomes. This toolkit integrates with scikit-learn pipelines to model censored datasets directly.

Yes, risk prediction with censored data works seamlessly within scikit-learn pipelines. You can chain preprocessing steps with models like CoxPHSurvivalAnalysis or RandomSurvivalForest to predict risk scores.

Survival models can be evaluated using Harrell's C-index, Uno's IPCW C-index, time-dependent AUC, and Brier score. These metrics measure ranking performance and prediction accuracy for censored time-to-event data.

Survival SVMs like FastSurvivalSVM offer ranking-based optimization, while Cox models estimate hazard ratios directly. Both handle censored biomedical datasets, but ensemble methods may capture non-linear effects better than standard Cox models.

Penalized Cox models like CoxnetSurvivalAnalysis are used for survival prediction when dealing with high-dimensional data or multicollinearity. They apply regularization to prevent overfitting while modeling censored time-to-event outcomes.

Yes, competing risks analysis is supported via cause-specific hazards and cumulative incidence function (CIF) estimation. This allows modeling multiple event types when standard survival analysis assumptions are insufficient.