This skill empowers Claude to perform advanced survival analysis, focusing on the unique challenges of censored data where event times are only partially known. It provides expert guidance and implementation patterns for a wide range of models, including Cox Proportional Hazards, Random Survival Forests, and Gradient Boosting, along with rigorous evaluation metrics like Uno's C-index and Brier scores. Whether you are analyzing clinical trial results, industrial equipment failure, or customer churn, this skill ensures best practices are followed for data preparation, model selection, and the interpretation of time-to-event results.
主要功能
01Comprehensive support for competing risks and censored data preprocessing
02Non-parametric estimation using Kaplan-Meier and Nelson-Aalen curves
03Specialized survival evaluation metrics including C-index and Brier score
04Advanced ensemble modeling with Random Survival Forests and Gradient Boosting
05324 GitHub stars
06Implementation of Cox Proportional Hazards and penalized Coxnet models
