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June 2, 2011
Biomarkers, Revisited. Unfortunately.
Your genome - destiny, right? That's what some of us thought - every disease was going to have one or more associated genes, those genes would code for new drug targets, and we'd all have a great time picking them off one by one. It didn't work out that way, of course, but there are still all these papers out there in the literature, linking Gene A with the chances of getting Disease B. So how much are those worth?
While we're at it, everyone also wanted (and still wants) biomarkers of all kinds. Not just genes, but protein and metabolite levels in the blood or other tissue to predict disease risk or progression. I can't begin to estimate how much work has been going into biomarker research in this business - a good biomarker can clarify your clinical trial design, regulatory picture, and eventual marketing enormously - if you can find one. Plenty of them have been reported in the literature. How much are those worth, too?
Not a whole heck of a lot, honestly, according to a new paper in JAMA by John Ioannidis and Orestes Panagiotou. They looked at the disease marker highlights from the last 20 years or so, the 35 papers that had been cited at least 400 times. How good do the biomarkers in those papers have to be to be useful? An increase of 35% in the chance of getting the targeted condition? Sorry - only one-fifth of the them rise to that level, when you go back and see how they've held up in the real world.
Subsequent studies, in fact, very rarely show anything as strong as the original results - 29 of the 35 biomarkers show a less robust association after meta-analysis of all the follow-up reports, as compared to what was claimed at first. And those later studies tend to be larger and more powered - in only 3 cases was the highly cited study the largest one that had been run, and only twice did the largest study show a higher effect measure than the original highly cited one. Only 15 of the 35 biomarkers were nominally statistically significant in the largest studies of them.
Ioannidis has been hitting the literature's unreliability for some time now, and I think that it's hard to dispute his points. The first thought that any scientist should have when an interesting result is reported is "Great! Wonder if it's true?" There are a lot of reasons for things not to be (see that earlier post for a discussion of them), and we need to be aware of how often they operate.
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