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Weak links

In Brief

Weak links

Ties between biomarkers and disease are often overstated

More than half of 35 of the most widely cited studies linking genes, proteins or other “biomarkers” to specific diseases vastly overstate the association, according to new research. As a result, clinicians may have been giving patients some bad advice.

What’s behind the faulty biomarker-disease connections? At least in part, they’re the result of statistical vagaries. But human nature and the competitive nature of scientific publication also play roles, says John Ioannidis, MD, DSc, chief of the Stanford Prevention Research Center, in a paper published June 1 in the Journal of the American Medical Association.

“No research finding has no uncertainty; there are always fluctuations,” he says. “This is not fraud or poor study design, it’s just statistical expectation. Some results will be stronger, some will be weaker. But scientific journals and researchers like to publish big associations.”

Once published, the perception of a strong link between a marker and a disease often persists. As subsequent studies repeatedly reference the landmark findings, they become accepted as incontrovertible even in the face of later, larger studies reporting less-spectacular or even statistically negligible associations.

Among the links that now look weaker based on larger follow-up studies include one tying the BRCA1 mutation to colon cancer, another that associates elevated C-reactive protein in the blood with cardiovascular disease and one that connects high homocysteine levels to vascular disease.

“We found that a large majority of these highly cited papers suggested substantially stronger effects than that found in the largest study of the same markers and outcomes,” says Ioannidis.

He notes that studies with greater numbers of patients or studies called meta-analyses, which compile the results of several independent studies, are more likely to be accurate than smaller studies. To use the example of flipping a coin, you might not be surprised to come up with two, three or even four heads in a row, but over the course of hundreds of flips you will approach a ratio of 50:50.

In addition to statistical aberrations, you also have the potential for superimposed bias, Ioannidis says. “Researchers tend to play with their data sets, and to analyze them in creative ways. We’re certainly not pointing out any one investigator with this study; it’s just the societal norm of science to operate in that fashion. But we need to follow the scientific method through to the end and demand replication and verification of results before accepting them as fact.”

 

 

E-mail Krista Conger

 

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