Confounding

A confounder is a variable in a study that distorts the true relationship between an exposure and an outcome, so it looks like the exposure and the outcome are either more associated or less associated than they really are.

For example, let’s say you hear on the news that drinking coffee is associated with developing heart disease, and - because you drink a lot of coffee - you decide to conduct a study to see if this is true.

First, you recruit 100 people that drink coffee and 100 people that don’t drink coffee, follow them for ten years, and then compare the number of people who developed heart disease in each group.

First, off you must really love coffee and be fairly wealthy to spend ten years studying it at the drop of a hat.

Now, let’s say that the proportion of people who develop heart disease in the coffee drinking group is - 50 out of 100, or 50% - and proportion of people who develop heart disease in the non-coffee drinking group - is 20 out of 100, or 20%.

Comparing 50% and 20%, you get a relative risk of 2.5, meaning the risk of developing heart disease for people that drink coffee is 2.5 times the risk for people that don’t drink coffee.

The association between coffee drinking and heart disease can be represented by an arrow pointing from the exposure to the outcome.

The arrow represents a potential causal relationship - in other words, coffee drinking potentially causes the development of heart disease.

But does drinking coffee really cause heart disease? Maybe, or maybe there’s a mysterious third variable - like smoking - that’s confounding the relationship, or making it look like there’s an association when there really isn’t one.

To be considered a confounder, two conditions have to be met.

The first condition is that a variable has to be associated with the exposure - meaning that the variable is seen to occur significantly more frequently among one group than the other.

So in this case, people that smoke would have to be either more or less likely to drink coffee compared to people that don’t smoke.

For example, 45 people - or 45% - in the coffee drinking group smoked compared to 5 people - or 5% - in the non-coffee drinking group, so people that smoked are 9 times more likely to drink coffee than people that don’t smoke.

Let’s suppose that smoking cigarettes makes people crave coffee, so 90% of people who smoke also drink coffee, while only 50% of people who don’t smoke also drink coffee.

This relationship can be represented by an arrow pointing from smoking to coffee drinking, since people that smoke are more likely to drink coffee than people who don’t smoke.

The second condition is that a confounder has to be associated with the outcome, so smoking would have to be associated with developing heart disease.

In our study, of the 50 people that smoked, 40 people - 80% - developed heart disease, and 10 people - 20% - didn’t develop heart disease.

So the risk of heart disease for people that smoke is 4 times higher compared to people that don’t smoke.

Since smoking damages the lining of blood vessels, it’s makes sense that people who smoke are more likely to develop heart disease than people that don’t smoke.

This relationship can be represented by drawing an arrow from smoking to heart disease, since an increase in smoking leads to an increase in heart disease.

So, looking at the diagram, we can see that an increase in smoking leads to an increase in coffee drinking and an increase in heart disease.

So even though it looks like there’s a relationship between the two variables, it’s hard to know whether or not heart disease is dependent on coffee, since the risk of heart disease also depends on whether or not a person smokes cigarettes.

In some cases, the mysterious third variable has a different relationship with the exposure - specifically that it’s caused by the exposure.

In that case, the variable may be considered a mediator.

For example, let’s say that we wanted to look at the relationship between obesity and heart disease, and let’s say that cholesterol levels are the third variable.

An increase in obesity is typically associated with an increase in cholesterol, and an increase in cholesterol is typically associated with an increased risk of heart disease.

The reason cholesterol isn’t a confounder in this situation is because simply increasing a person’s cholesterol doesn’t necessarily change a person’s weight, so cholesterol has no influence on obesity.

So, here, we could essentially take cholesterol out of the diagram and we’ll still see the true association between obesity and heart disease.

Confounding generally occurs when the two groups being compared aren’t similar to one another - like if there are more smokers in one group than the other.

There are three methods you can use when designing your study to make sure that the two study groups are similar: randomization, restriction of the study population and matching.

Randomized controlled trials use a tool called randomization, meaning individuals get selected to each study group through a process of chance.

Using randomization, there’s a pretty high chance that each group will have similar characteristics.