Performs comprehensive survival analysis and time-to-event modeling using the scikit-survival library in Python.
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 on fitting Cox proportional hazards models, implementing ensemble methods like Random Survival Forests and Gradient Boosting, and training Survival Support Vector Machines. Whether you are conducting clinical research, analyzing equipment failure, or predicting customer churn, this skill offers standardized workflows for data preprocessing, non-parametric estimation, and rigorous model evaluation using specialized metrics like the concordance index and Brier score.
Key Features
01Implementation of standard and penalized Cox Proportional Hazards models
02Non-parametric estimation via Kaplan-Meier and Nelson-Aalen curves
03Ensemble survival modeling with Random Survival Forests and Gradient Boosting
04Handling of censored data and competing risks analysis
05Advanced evaluation using Harrell's and Uno's Concordance Index
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Use Cases
01Predicting patient outcomes and survival probabilities in medical research
02Analyzing time-to-failure for predictive maintenance in industrial settings
03Modeling customer retention and churn duration for subscription services
