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Summarizing Data:
EFFECT STATISTICS continued

Difference in Frequency
We seldom use the raw counts of something when we compare frequencies. In the example shown on the right, there may have been 600 non-smokers who had heart disease, out of a sample of 2000 non-smokers altogether, so we usually talk about 30% of the non-smokers having heart disease. A percent frequency makes it easier to compare the rate of heart disease in other groups, for example smokers. But how do we now actually compare the frequencies? The simplest way is to subtract them: the difference in rate of heart disease is 45%. I think that's the best way, but it is not the usual way. Instead, researchers usually divide one frequency by the other. In the example, smokers would be 75/30, or 2.5 times as likely to develop heart disease as non-smokers. Or to put it another way, the relative risk of developing heart disease for smokers is 2.5. If the frequency of heart disease was the same in both groups, the relative risk would be 1.0, and if the frequency was less in smokers, the relative risk would be less than 1.0.

It's hard to put a figure on what are considered small, medium and large differences between the frequencies of something in two groups, because it depends on the frequencies. If one group has about 50% with a characteristic, a frequency of 60% or 40% in the other group can be considered small. That difference corresponds to a relative risk of about 1.2 (or 0.8, depending which way around the frequencies are). Once the frequencies get low (e.g. 1% in one group), relative risks have to be 2 or more before people get excited.

Notice that the two groups differ in exposure to something that might cause the disease. A somewhat different statistic, the odds ratio, is used when the basis of the grouping is whether subjects already have the disease: in other words, when the groups are cases and controls. In the example shown, the odds of being a smoker in the heart-disease group are 75/25 = 3. Similarly, the odds of being a smoker in the healthy group are 30/70 = 0.43. The odds ratio is therefore 3/0.43 = 7. Interpret this statistic as "seven people with heart disease smoke for every healthy person who smokes". Or, if you had two people in front of you, a healthy person who smokes and a person with heart disease, you would break even in the long run by betting at odds of 7:1 that the person with heart disease is a smoker. Fine, but I still have trouble getting my brain around this statistic. Are those odds good or bad, in terms of the effect of smoking on heart disease? I don't know. I guess I don't work with this statistic enough to have a feel for it. (I used to have here "seven smokers have heart disease for every one smoker who doesn't" or "if you are a smoker, odds are 7 to 1 that you have heart disease", but these interpretations are wrong. Thanks, Chris Rhoads!).

Coming up next is the important question of how big is big in effect statistics.

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Last updated 16 March 02