scikit-survival

To perform survival analysis on censored time-to-event data, you can use scikit-survival to prepare data in the Surv format and fit models like CoxPHSurvivalAnalysis to generate risk scores. It supports scikit-learn pipelines for integrated data handling.

The best way to evaluate a Cox model is by calculating the concordance index, Uno's C-index, and Brier score. The scikit-survival toolkit integrates with scikit-learn to provide these specific evaluation metrics for survival analysis.

Yes, you can use random survival forests for non-linear relationships in time-to-event datasets. The toolkit provides ensemble methods like RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and ExtraSurvivalTrees to model complex data.

Yes, scikit-survival supports competing risks by calculating cumulative incidence functions (CIFs) and cause-specific hazards. This allows you to analyze datasets where multiple distinct events can occur.

Survival SVMs rank patients by optimizing a hinge loss function for survival data. The toolkit includes FastSurvivalSVM, FastKernelSurvivalSVM, and HingeLossSurvivalSVM to perform SVM-based ranking across research datasets.

For Python survival analysis, penalized variants available for Cox models include CoxnetSurvivalAnalysis and IPCRidge. These models extend the standard Cox proportional hazards approach to handle high-dimensional data.