Should we adjust for a confounder if empirical and theoretical criteria yield contradictory results a simulation study?
To conclude, theoretical confounders should be adjusted for regardless of the empirical evidence found. The adjustment for factors that do not have a confounding effect minimally effects. Potential confounders affected by both exposure and outcome should not be adjusted for.
How to control the effect of confounding variables?
There are various ways to modify a study design to actively exclude or control confounding variables (3) including Randomization, Restriction and Matching. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders.
How is an interaction different from a confound?
Interaction differs from confounding in that it your exposure/outcome relationship is different on different levels of a third variable. Essentially, rather than the third variable influencing the OR like a confounder does, the third variable will have different ORs for different categories. An easy example is using gender (Male/Female).
What does it mean to adjust for confounding in multivariable analysis?
To understand the multiple factors that can influence the outcome. To recognize and adjust for confounding. If other factors that influence the outcome are unevenly distributed between the groups, these other factors can distort the apparent association between the outcome and the primary exposure of interest; this is what is meant by confounding.
When to include confounding variable in multivariable logistic regression?
If you are analyzing data using multivariable logistic regression, a rule of thumb is if the odds ratio changes by 10% or more, include the potential confounder in the multi-variable model. The question is not so much the statistical significance, but the amount the confounding variable changes the effect.