scikit-survival

Time-to-event modeling handles censored data by using specific survival analysis utilities to create survival outcomes, preprocess data, and validate quality before fitting models. This approach accommodates observations that haven't experienced the event by the study's end.

Evaluating survival analysis model performance involves calculating metrics like the Concordance Index (C-index), time-dependent AUC, and Brier Score. These metrics assess how accurately the model predicts the relative risk and discriminates between survival times.

Yes, you can fit both Random Survival Forests and Cox Proportional Hazards models within the same workflow. The toolkit supports model selection across various algorithms including Gradient Boosting and Survival SVMs, allowing you to compare performance directly.

Yes, competing risks analysis is supported alongside standard survival analysis and time-to-event modeling. This allows you to model scenarios where multiple distinct event types can occur, preventing the first event from censoring the others.

You should use Gradient Boosting instead of a Cox model when your data violates the proportional hazards assumption or exhibits complex non-linear relationships. Gradient Boosting captures complex patterns that a standard Cox model cannot.

To predict time to disease recurrence, you fit a survival model to patient data, explicitly accounting for right-censored observations where patients haven't experienced recurrence. The model evaluates risk factors and estimates event times using the available data.