What are the Challenges of Multivariable Adjustment?
Despite its usefulness, multivariable adjustment has several challenges:
1. Selection of Confounders: Deciding which variables to include in the adjustment can be difficult. Including irrelevant variables may introduce noise, while omitting important confounders can lead to biased results.
2. Collinearity: When two or more variables are highly correlated, it can be challenging to separate their individual effects. This issue, known as collinearity, can complicate the interpretation of the results.
3. Sample Size: Adequate sample size is crucial for multivariable adjustment. Small sample sizes can lead to overfitting and unreliable estimates.
4. Missing Data: Missing data is a common issue in cancer research. Strategies such as imputation can be used, but they require careful consideration to avoid introducing bias.
