The New Gastroenterologist

Effect modification: An important, but often underappreciated, statistical concept


In previous articles, we explored the meaning of two statistical concepts: that of the P value1 and that of confounding.2 In today’s article, we will focus on another important, though often underrecognized, statistical concept: that of effect modification (sometimes also called “interaction,” although many authors draw a distinction between these terms).3-5 In brief, effect modification is the recognition that the relationship between two variables can be different based on the values of a third variable. Let me illustrate with an example. Suppose that you give a weight-loss drug to a group of people. After a predetermined time, say 3 months, you weigh them to measure the effects of the drug, and you record that on average each individual lost about 10 pounds. Suppose, however, that you are curious to see the effect of the drug in men and women separately. You perform an analysis stratified by sex, and you notice that men lost on average only about 1 pound each, whereas women lost on average about 30 pounds each. What do these results mean? It seems that the effect of the drug on weight loss is different depending on the value of a third variable, namely sex. That is, in this case, sex acts as an effect modifier. Based on this analysis, we may conclude that the drug has real effects on women but not on men.

Dr. Manol Jovani, University of Kentucky Albert B. Chandler Hospital, Lexington

Dr. Manol Jovani

Before reaching this conclusion, however, it is appropriate to ask whether we have made a mistake. The first obvious thing is to make sure that we have not made a mistake in our collection of data. Once we have excluded the presence of structural bias in our dataset, how can we ascertain that these results, which at eyeballing seem so different, have not been so as a result of chance? Fortunately, we do not have to guess. There is a way to formally test for this hypothesis. If sex is truly an effect modifier, then we can perform what is called in statistical terms an “interaction term” between the exposure (in this case the drug) and the potential effect modifier (in this case sex) in a multivariable model that includes both as exposures. If the P value for that interaction term is less than .05, then the interaction term is statistically significant, and therefore the variable (in this case sex) is confirmed to be an effect modifier. Hence, the results are not due to chance, and the different effects in men and women are plausibly attributable to different biological responses to the medication.


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