Epidemiologists often face complex datasets involving numerous potential risk factors and confounders. Traditional regression methods may struggle to handle such high-dimensional data effectively. Lasso offers several advantages in this context:
Variable Selection: By shrinking less important coefficients to zero, Lasso helps in identifying the most relevant risk factors. Reduction of Overfitting: Regularization reduces the chances of overfitting, thereby enhancing the model's predictive performance. Interpretability: Lasso simplifies models by including only the most significant variables, making them easier to interpret. Handling Multicollinearity: Lasso is effective in situations where explanatory variables are highly correlated, a common occurrence in epidemiological studies.
