What are the Challenges of Multivariable Adjustment?
While multivariable adjustment is a powerful tool, it comes with several challenges:
1. Multicollinearity: When two or more covariates are highly correlated, it can be difficult to distinguish their individual effects, leading to unstable estimates. 2. Overfitting: Including too many covariates can lead to overfitting, where the model becomes too tailored to the specific data set and may not generalize well to other populations. 3. Data Quality: Accurate adjustment requires high-quality data. Missing or inaccurate data on key covariates can compromise the validity of the adjustment. 4. Model Selection: Choosing the appropriate variables to include in the model is crucial. Including irrelevant variables can introduce noise, while omitting important confounders can lead to biased results.
