alterlab-scikit-survival

To handle censored data for survival analysis in Python, you create a Surv object to structure your time-to-event outcomes, then fit models like CoxPHSurvivalAnalysis to process the censored observations and predict risk scores.

To compare Cox models and Random Survival Forests for time-to-event modeling, fit both algorithms on your prepared survival data and evaluate their predictive performance using metrics like the concordance index, time-dependent AUC, and Brier scores.

Yes, you can model competing risks using scikit-survival by calculating cumulative incidence functions (CIFs), which allow you to estimate the probability of specific event types occurring over time in the presence of other competing events.

You evaluate survival model predictions using the concordance index to measure ranking accuracy and the Brier score to assess prediction calibration over time, applying these metrics to risk scores or survival probabilities generated by your fitted models.

Yes, scikit-survival supports penalized Cox regression via CoxnetSurvivalAnalysis and Survival SVMs via FastSurvivalSVM, allowing you to apply these advanced ensemble and machine learning methods to high-dimensional clinical studies.

You should use Gradient Boosting Survival Analysis instead of standard Cox proportional hazards models when your data violates proportional hazards assumptions or requires capturing complex non-linear relationships for robust risk prediction.