High-dimensional data: Modern epidemiological studies often involve large datasets with numerous potential predictors, such as genomic data, lifestyle factors, and environmental exposures. Penalized regression helps manage these high-dimensional datasets effectively. Multicollinearity: When predictors are highly correlated, traditional regression models can produce unstable estimates. Penalized regression mitigates this issue by shrinking the coefficients of correlated predictors. Overfitting: Adding a penalty term helps prevent overfitting, which is crucial when the model is trained on small sample sizes but contains many predictors.
