Several strategies can be employed to address multicollinearity in epidemiological studies:
Variable Selection:Removing one of the correlated variables from the model can help reduce multicollinearity. This decision should be based on theoretical considerations and domain knowledge. Principal Component Analysis (PCA): PCA can be used to transform the correlated variables into a set of uncorrelated components, which can then be used as predictors in the regression model. Ridge Regression: This technique adds a penalty to the regression model to shrink the coefficients of the correlated predictors, thereby reducing multicollinearity. Centering the Data: Subtracting the mean from each predictor variable can sometimes help reduce multicollinearity, especially when interaction terms are present.
