scikit-survival

Build survival analysis models with censored data in Python by using scikit-survival. Fit models like CoxPHSurvivalAnalysis or RandomSurvivalForest on time-to-event datasets, leveraging integration with pandas and scikit-learn for preprocessing.

Time-to-event models are evaluated using metrics like the concordance index (C-index), time-dependent AUC, and Brier score. These metrics measure ranking performance and prediction accuracy over time for censored survival data.

Yes, you can model nonlinear survival data using ensemble methods like RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and ExtraSurvivalTrees to capture complex relationships that classic regression models miss.

Scikit-survival supports competing risks workflows alongside comprehensive preprocessing and evaluation. It provides specialized models and metrics to handle multiple distinct failure types within time-to-event analysis.

Use Survival SVMs like FastSurvivalSVM for margin-based learning when you need to optimize ranking performance directly, instead of using classic Cox models that rely on proportional hazards assumptions for survival analysis.