Why is it Important?
This criterion is particularly important in epidemiological research because it helps to ensure that the estimates of association between exposures and outcomes are not biased by confounding variables. By applying the change in estimate criterion, researchers can make more informed decisions about which variables to adjust for, thereby improving the validity of their results.
How is it Applied?
The process involves comparing the estimate of the association of interest with and without the potential confounder in the model. If the difference between these two estimates exceeds a pre-specified threshold (commonly 10%), the variable is considered a confounder and is included in the model.
Example Application
Consider a study investigating the relationship between smoking and lung cancer. Researchers might initially control for age and gender. To determine whether occupational exposure is a confounder, they add it to the model and observe the change in the estimate of the smoking-lung cancer association. If the inclusion of occupational exposure changes the estimate by more than 10%, it is considered a confounder and retained in the model.Advantages
1. Objectivity: It provides a quantitative method for decision-making, reducing the subjectivity in choosing confounders.
2. Simplicity: This criterion is relatively easy to understand and apply, making it accessible for epidemiologists.
3. Focus on Precision: By ensuring only significant confounders are included, it helps to maintain the precision of the estimates.Limitations
1. Arbitrary Threshold: The commonly used 10% threshold is somewhat arbitrary and may not be appropriate for all studies.
2. Model Dependence: The criterion depends on the specific model and may not generalize to different models or datasets.
3. Overfitting: Including too many variables, even if they meet the criterion, can lead to overfitting, especially in small sample sizes.Comparison with Other Methods
Other methods for identifying confounders include directed acyclic graphs (DAGs) and statistical tests like p-values and confidence intervals. While DAGs provide a more comprehensive understanding of causal relationships, they require extensive subject matter knowledge. On the other hand, statistical tests can sometimes be misleading, especially in small sample sizes. The change in estimate criterion offers a middle ground by being relatively straightforward and based on changes in the primary measure of association.Conclusion
The change in estimate criterion is a valuable tool in the field of epidemiology for identifying confounders and ensuring accurate estimates of associations. While it has its limitations, its simplicity and objectivity make it a popular choice among researchers. By carefully applying this criterion, epidemiologists can enhance the validity and reliability of their findings.
