This analysis compares the standard Cox proportional hazards model with a flexible parametric Royston–Parmar model using the lung cancer dataset from the survival package. We fit both models using age and sex as covariates, extract the cumulative baseline hazard from the Cox model, estimate the hazard function from the Royston–Parmar model, and visualize both on the same graph for comparison. The study highlights differences between stepwise and smooth hazard estimates and demonstrates the interpretability of flexible parametric survival models.

Extends the analysis with non-parametric and semi-parametric methods — Kaplan–Meier estimation, Nelson–Aalen cumulative hazard, formal hypothesis testing, full Cox regression with diagnostics, model refinement for PH violations, and a comprehensive synthesis integrating all three analytical frameworks.

Parametric survival analysis of the rats dataset using R. Covers Kaplan–Meier estimation, Cox regression, parametric model selection (Weibull best by AIC), life functions, and frailty modelling to account for litter clustering.

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