Reducing Confounding During Data Analysis

Lead Author(s): Jeff Martin, MD

Methods for Reducing Confounding During Data Analysis: Stratification

In the data analysis phase, we can stratify to reduce confounding.

An example of stratification can be found in the Matches, Smoking, and Lung Cancer Study.

Do NOT Adjust - Qualitative Interaction

Stanton provides an example of a study that looked at the effects of caffeine consumption on delayed conception.

This is qualitative interaction. Do NOT summarize these numbers.

Third Variable: Decision Tree - Confounding

Determining the third variable: To do this, you will form an adjusted summary estimate of the two or more stratum-specific estimates and you will compare the adjusted measure of association to the crude measure of association.


Underlying Assumption for a Summary Estimate

If the relationship between the exposure and the outcome varies meaningfully in a clinical/biologic sense across strata of a third variable: When you summarize across strata, the assumption is that no interaction is present.

Inappropriate Statistical Testing for Confounding

Statistical testing for confounding is inappropriate when we know the variable is a known confounder or the study has a small sample size.