Difference between confounding and effect modification
At this point someone might ask, “What then is the difference between confounding and effect modification? In both cases, we do stratified analysis of the relationship between an exposure (in this case the weight-loss drug) and an outcome (in this case weight loss) based on strata of a third variable (in this case sex).” The difference is fundamental. Confounding, as we explored in a previous article,2 is something that we would like to get rid of. It is the effect of an outside variable on both the exposure and outcome that does not allow us to properly evaluate the real relationship between the two. As such, we try to adjust for this variable, so that its effect is eliminated and we can only observe the relationship between the exposure and the outcome. Effect modification, on the other side, is not something we would like to get rid of. On the contrary, effect modification is part of what we would like to explore and describe because it is part of the biological mechanism that explains the real relationship between the exposure and the outcome. In the example above, if effect modification by sex is confirmed, it implies that there is something in female biology that is not found in male biology (or vice-versa) that makes their response to the medication different and therefore something of interest to study further. Thus, differently from confounding, effect modification is part of the objective reality of the world which we would like to explore and evaluate.
Several questions then arise. How can we know whether a variable is a confounder, an effect modifier, or both? As a general rule of thumb, a confounder would be a variable which, when a stratified analysis is done (or when added to a multivariable model), will change the relationship between the exposure and outcome by 10% or more. However, the relationship between the exposure and the outcome in both strata will be similar. In the example above, it would mean that if sex was only a confounder, then stratifying by sex would show roughly a similar change in the effect (say 9 kg for men and 11 kg for women). An effect modifier on the other hand is one which, when a stratified analysis is done, the association between the exposure and the outcome is very different in the two strata, as illustrated in the example above (for simplicity I am only considering two-level effect modifiers in this article).
Can a variable be both a confounder and an effect modifier? Yes, that is possible. What can be done in this case? The most common approach is to behave the same as when only effect modification is present, namely to show with the interaction term that effect modification exists and present the results between the exposure and outcome separately by the level of the effect modifier (in this example, it means that we need to describe the effects of the weight-loss drug separately for men and women). The stratified analysis/presentation will, by definition, take care of confounding as well.2
Should we always look for effect modification? Not necessarily. As a general rule, we need to test for effect modification only if there is some biological rationale that would compel us to do so, and testing should be hypothesis-driven. A common mistake that some authors make is to perform too many interaction tests and then describe as “positive findings” any test for which P value happens to be less than .05. However, as we pointed out in the article on P value,1 if we perform multiple tests, this increases the probability of false positives, and therefore the probability of spurious findings. Thus, effect modification analysis (with interaction terms) should, generally speaking, be performed with a biological rationale and/or be hypothesis driven.
Conclusion
Effect modification is an essential statistical concept that describes an underlying biological reality in which the association between an exposure and an outcome is different based on the values of a third variable. Differently from confounding, which clouds the association between exposure and outcome, and therefore is something that we try to get rid of, effect modification serves to bring to light a more proper understanding of the biological reality underlying the true association between an exposure and an outcome and as such is something that needs to be explored and described.
Dr. Jovani is assistant professor of medicine, therapeutic endoscopy, digestive diseases, and nutrition at the University of Kentucky Albert B. Chandler Hospital, Lexington. He has no conflicts of interest.
