Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide

Survival analysis provides tools for modeling time-to-event data, particularly when some data is "censored," meaning the event did not occur during the observation period. Key concepts include the survival function, the probability of surviving past a certain time, and the hazard function, the instantaneous rate of failure. The Kaplan-Meier estimator can be used to estimate survival curves directly from data, while the Cox proportional hazards model offers a semi-parametric regression approach to understand how covariates affect the hazard rate. The model's utility is demonstrated by fitting it in Python on a recidivism dataset and interpreting the resulting hazard ratios for factors like financial aid and age.