scikit-survival

To build a survival model with censored data in Python, use scikit-survival to create structured targets with Surv.from_arrays, then fit models like Cox proportional hazards or Random Survival Forests that correctly account for right-censored observations.

Evaluate survival models on censored datasets using censoring-aware metrics like Harrell's and Uno's C-index, cumulative_dynamic_auc for time-dependent AUC, and integrated_brier_score to measure prediction accuracy over time.

Yes, you can estimate competing risks cumulative incidence functions in Python using scikit-survival, which supports cause-specific analyses and cumulative incidence estimation for datasets where multiple event types can occur.

Yes, scikit-survival supports Random Survival Forests and Gradient Boosting for time-to-event analysis, alongside penalized Cox models, accelerated failure time (IPCRidge), and Survival SVMs for right-censored data.

Compare Cox models with survival SVMs on clinical datasets by fitting both using scikit-survival, then evaluating their discrimination and calibration using Uno's C-index and Integrated Brier score to determine which generalizes better.

You need structured targets for survival analysis because censored data requires both event time and censoring indicators. Using Surv.from_dataframe or Surv.from_arrays pairs these values so models correctly handle missing event information without bias.