scikit-survival

Survival analysis on censored time-to-event data in Python is performed using scikit-survival to fit models like CoxPHSurvivalAnalysis, evaluate with concordance index, and estimate survival curves. It supports biomedical, reliability, and research applications.

Yes, time-to-event analysis supports ensemble methods like RandomSurvivalForest and GradientBoostingSurvivalAnalysis. These models handle censored data directly, providing risk scoring and survival curve estimation for complex datasets.

Censored survival models are evaluated using metrics like the concordance index, Uno's C-index, and integrated Brier score. These metrics assess model discrimination and calibration accuracy for time-to-event predictions.

Yes, scikit-survival supports survival SVMs through FastSurvivalSVM and FastKernelSurvivalSVM. These models enable efficient risk scoring and survival prediction on high-dimensional censored time-to-event data.

Penalized Cox models for high-dimensional survival data are handled using CoxnetSurvivalAnalysis. This model applies elastic-net regularization to Cox proportional hazards, preventing overfitting while estimating risk scores.

Cox models assume proportional hazards, which limits their use when hazard ratios change over time. For non-proportional hazards or competing risks, non-parametric estimators or ensemble survival methods like RandomSurvivalForest may be more appropriate.