scikit-survival

Survival analysis on censored data in Python is performed by fitting models like Cox proportional hazards or Random Survival Forests to time-to-event outcomes. The scikit-survival toolkit provides the necessary functions to preprocess data, fit models, and evaluate results.

For time-to-event modeling, you can fit Cox models, Random Survival Forests, Gradient Boosting, and Survival SVMs. These models handle censored observations and predict the time until a specific event occurs.

Evaluate a Cox model with censored observations using metrics like the concordance index, Brier score, and time-dependent AUC. These metrics measure how well the model predicts the ranking and probability of survival over time.

Yes, scikit-survival supports handling competing risks in survival data. The toolkit allows you to model time-to-event outcomes when multiple distinct events can occur, preventing the first event from censoring the others.

Predicting disease recurrence time is best achieved by fitting survival models to patient data. You can use Random Survival Forests or Gradient Boosting to account for censored observations and estimate the time to recurrence.

Yes, it requires creating specific survival outcomes and validating data quality before fitting models. The toolkit provides functions to preprocess data into the required structured format for time-to-event modeling.