scikit-survival

You can model survival data with censored observations in Python using scikit-survival to fit Cox proportional hazards, Random Survival Forests, gradient boosting, and support vector machines. It handles censored time-to-event data directly for robust analysis.

Survival model performance can be evaluated using concordance indices, time-dependent AUC, and Brier scores. These metrics assess model discrimination and calibration, enabling you to benchmark and validate predictions on censored data effectively.

To prepare raw time-to-event data for survival analysis, you must create survival outcomes from your raw data, handle missing values, and encode categorical variables. This preprocessing ensures datasets are correctly structured for fitting survival models.

Yes, scikit-survival works with scikit-learn for machine learning tasks. It builds upon scikit-learn, numpy, and pandas, allowing you to integrate ensemble methods like Random Survival Forests and gradient boosting within your existing Python machine learning workflows.

The best way to compare Cox proportional hazards with ensemble survival models is by fitting both using scikit-survival, then evaluating their performance using concordance indices and Brier scores. This provides robust metrics to identify the most accurate model.

You should use a Random Survival Forest instead of a Cox model when your survival data has complex, non-linear relationships that violate proportional hazards assumptions. Ensemble methods like gradient boosting capture these intricate patterns better than linear models.