The standard way we have of evaluating a test is to look at its

effects in a large population, and

often in a large population of patients we have split up into two groups.

Those groups can be split up by nature, so

they could be patients who have a disease and patients who don't have a disease,

patients who take a drug, patients who don't take a drug.

Or sometimes we take a large population and we randomly assign them

to get a drug or to not get a drug, that's a randomized clinical trial.

That's the fundamental basis of deciding whether a test or

a drug is actually going to be useful in long term treatment.

So the idea is to compare the group on the right to the group on the left.

Now as we do that, we have to make sure that the groups

are chosen appropriately and comparable in other ways.

So you have to make sure, for example,

that the patients who take a drug are the same as the patients who don't

take a drug with respect to how their disease was determined,

when their disease was determined, how long they had the disease.

Whether they're African American or European can make a huge difference.

You don't want all women in one group and all men in another group.

You don't want one group to have all the diabetics and

one group not to have the diabetics.

You don't want one group to take one treatment and

another group to take another treatment.

The time honored way of doing that,

of assigning patients to one of two groups, is a randomized trial.

Sometimes nature does the randomization for us, though.

For example, if we're examining the effect of a genetic variant,

we don't have control over that.

So we compare patients with a genetic variants to patients without

a genetic variant, and we try to ensure that all those other what we would call

comorbidities, or cotherapies, are similar in the two groups of patients.

So that's a very important concept in trial design and trial evaluation.

So here we are with two groups that we're going to compare.

Now pretend that one group, both groups have 50 patients in them.

I've counted them and I made this slide.

So one group has in the group on the left,

30 out of the 50 patients have a particular trait.

And the group on the right, 15 have the particular trait.

A common way of expressing the difference between those two groups is odds ratios or

relative risks.

So the odds ratio is the ratio of the odds in the two groups.

The odds in one group is 1.5, so 30 out of 50 patients have the trait,

20 out of 50 don't, so 30 divided by 20 is 1.5.

That's the odds in one group.

The odds in the other group are 15 divided by 35, and that's 0.42.

The ratio of those two is 3.57.

So the odds ratio for that particular test,

to distinguish between one group and another is 3.57.

The relative risks are calculated in this same way.

The risk in one group is 0.6.

The risk in the other group is 0.3.

And so the relative risk is 2.0.

So odds, ratios, and relative risks are common ways of expressing the impact for

example of a genetic variant on a trait in a population.