Agent skill · research science · aj-geddes
Survival Analysis
Analyze time-to-event data, calculate survival probabilities, and compare groups using Kaplan-Meier and Cox proportional hazards models
Why this skill is useful
Provides specific implementations of survival analysis techniques using Python libraries that the AI wouldn't reliably generate on its own.
What it needs
Requires lifelines, matplotlib, numpy, pandas, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Survival Analysis Overview Survival analysis studies time until an event occurs, handling censored data where events haven't happened for some subjects, enabling prediction of lifetimes and risk assessment. Key Concepts Survival Time: Time until event Censoring: Event not observed (subject dropped out) Hazard: Instantaneous risk at time t Survival Curve: Probability of surviving past time t Hazard Ratio: Relative risk between groups Common Models Kaplan-Meier: Non-parametric survival curves Cox Proportional Hazards: Semi-parametric regression Weibull/Exponential: Parametric models Log-rank Test: Comparing survival curves Competing Risks: Multiple event types Implementation with Python Censoring Types Right censoring: Event hasn't occurred (most common) Left censoring: Event occurred before observation Interval censoring: Event in unknown time interval Model Comparison Kaplan-Meier: Describes, doesn't explain Cox Model: Adjusts for covariates, proportional hazards Parametric: Assumes distribution Competing Risks: Multiple event types Applications Clinical trials Equipment reliability Customer churn Employee retention Product lifetime Deliverables Kaplan-Meier survival curves Survival probability estimates Log-rank test results Cox model coefficients Hazard ratios Risk stratification groups Survival predictions Model diagnostics
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/survival-analysis