We have discussed how to identify confounding,

and separately, how to identify effect modification.

I'm confident that by now,

you're familiar with each process.

But things seem to get a bit confusing when you have to assess

both confounding and effect modification in the same study.

In reality, there's absolutely no reason to get confused, and I will explain why.

In a typical study,

we have an exposure and an outcome.

Let's also consider a third extraneous variable.

I call it extraneous because it is neither the exposure nor the outcome.

It could be something like sex or race, for example.

You would like to explore whether the extraneous variable is a source

of confounding or effect modification or maybe both.

The first thing to do would be to stratify the data by the extraneous variable,

and estimate the association between the exposure and the outcome in each stratum.

In practical terms, this means that you obtain an odds ratio for men and one for women,

if sex is the extraneous variable of interest, of course.

If the odds ratio for men is similar to the odds ratio for women,

then based on the definition,

there's obviously no effect modification by sex,

while the question whether there is confounding by sex is still open.

Using the stratum-specific odds ratios,

you can estimate an adjusted odds ratio, adjusted for sex.

If the adjusted odds ratio is similar to the crude or unadjusted odds ratio,

there is probably no confounding by sex,

and you don't need to take any further action.

But if the adjusted odds ratio differs considerably from the unadjusted estimate,

this may be an indication of confounding,

and you should control for it by presenting the adjusted estimate.

What happens if the stratum-specific estimates are different?

In our example, what should you do if the odds ratio for

men is statistically different from the odds ratio for women?

The answer is straightforward.

This is a textbook case of effect modification.

Therefore, you will just report the stratum-specific odds ratios separately.

Again, the question whether sex is also a confounder has not been answered at this stage.

However, if you're presenting separate estimates for men and women,

which you do, because there is effect modification,

you don't really care if sex can cause confounding.

In practice, you have already controlled for

confounding by presenting stratum-specific odds ratios.

This strategy should allow you to

identify confounding and effect modification in a study.

If you think about it,

I haven't given you any new information,

you already know a few methods to assess confounding and you described one of them,

while also repeating the method to identify effect modification.

I could have said that you need to assess whether

there's confounding based on any of the usual methods,

and afterwards, to go through the usual process to identify effect modification.

In some cases, you might find that a certain variable is

both a confounder and an effect modifier, which is possible.

In summary, confounding is a problem of our study,

and therefore, we try to control for it.

Whereas, effect modification is a natural phenomenon,

which requires the presentation of stratum-specific estimates.