When multicollinearity is detected, several strategies can be employed to address it:
1. Remove Highly Correlated Predictors: One straightforward approach is to remove one of the highly correlated variables. For example, if both BMI and waist circumference are highly correlated, you might choose to keep only one in the model.
2. Combine Predictors: Sometimes, combining correlated variables into a single predictor can help. For example, creating a composite score from related biomarkers can reduce multicollinearity.
3. Principal Component Analysis (PCA): PCA transforms the correlated predictors into a set of uncorrelated components. These components can then be used as predictors, thus eliminating multicollinearity.
4. Ridge Regression: This technique adds a penalty to the regression model for large coefficients, which helps to reduce the impact of multicollinearity and stabilize coefficient estimates.
5. LASSO Regression: LASSO (Least Absolute Shrinkage and Selection Operator) not only penalizes large coefficients but also performs variable selection by shrinking some coefficients to zero, effectively reducing multicollinearity.
