For example, if your two risks are 0.01% and 0.02%, the risk ratio is 2 and the odds ratio is: (0.02/99.98)/(0.01/99.99) = 2.0002 Barely different
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But if the two risks are 20% and 40%, the risk ratio is still 2 (40/20) but the odds ratio becomes VERY different: (40/60)/(20/80) = 2.67 That's a lot higher!pic.twitter.com/73Fc7CxeUU
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Going back to this headline that I picked up - it looks at a study that used logistic regression, which spits out odds ratiospic.twitter.com/8vHaEBaLIS
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The results were reported as odds, with vapers having a 1.83 times higher odds of stroke than non-vapers Given that the prevalence of stroke was 2-4% in the groups, that means that the risk ratio would be a bit lower than 1.83
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In other words, the headline rounds UP from 83% to a 100% increase (double), but in actual fact it's more likely that the risk is somewhere around 75% instead!
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And this is done almost ubiquitously across the board It's not really the media's fault - scientists do it all the time as well!pic.twitter.com/BjxCDlughU
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It's also really hard to tell if the study used risks or odds unless you actually read it, which adds a layer of complexity to the matter
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Honestly, I want to end on a nice easy note, but the fact is that odds are confusing, a lot of researchers get them wrong, and it's unlikely we'll have a solution to this any time soon Hurray!
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Anyway, a reasonable proportions of the headlines you've seen probably overestimate the actual risk because the studies used odds ratios
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Replying to @GidMK
I have noticed many studies are reporting OR much more than RR in epi studies I’ve been reading lately. (Some HR, but mostly OR lately.) I’m not sure why. Maybe the higher number is more impressive for publication (part of publication bias)?
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My bet is that it's because everyone dichotomizes their outcomes and uses logistic regression
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