Why is Multicollinearity a Problem in Epidemiology?
Multicollinearity can lead to several issues in epidemiological studies:
1. Inflated Variance: High correlation between predictors inflates the variance of the coefficient estimates, making them unstable and unreliable. 2. Insignificant Results: Predictors may appear statistically insignificant even if they are important, due to the shared variance being attributed to other correlated predictors. 3. Reduced Interpretability: It becomes challenging to determine the independent effect of each predictor on the outcome, complicating public health interventions and policy recommendations.
