Regularized Cox Regression

What is regularized cox regression? 

Regularized cox regression is a statistical method used in survival analysis to model the relationship between predictor variables and the time to an event (such as death, disease progression, or failure) while addressing issues like overfitting. This approach applies regularization techniques, such as Lasso or Ridge regression, to the standard Cox proportional hazards model to improve model stability, enhance generalizability, and select relevant predictors by shrinking less important variables toward zero. 

Why is regularized cox regression important? 

Regularized cox regression enhances traditional survival analysis by addressing common challenges like overfitting and multicollinearity. It helps researchers and practitioners build more accurate, interpretable, and generalizable survival models, which is essential for analyzing high-dimensional data, such as patient characteristics or genetic factors, and making data-driven decisions in clinical and research settings.