"Biomarkers revealed as red herrings are still treated like smoking guns"

[Dolphin]

John Ioannidis was one of the authors. He's an interesting researcher in my opinion. He has a degree in mathematics. He often challenges the status quo.

Value of Disease Clues from Biomarkers Often Exaggerated

Biomarkers revealed as red herrings are still treated like smoking guns.

| May 31, 2011

By Heidi Ledford of Nature magazine


An analysis of nearly three-dozen highly cited papers has found that researchers often overstate the link between biomarkers and disease by citing papers that report the strongest association, even when subsequent analyses downplay the connection.

Full article at: http://www.scientificamerican.com/a...sease-clues-from-biomarkers-often-exaggerated

 

[Dolphin]

Abstract

Here's the abstract - but the other article is easier to read

Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses.

JAMA. 2011 Jun 1;305(21):2200-10.

Ioannidis JP, Panagiotou OA.

Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA. jioannid@stanford.edu

Abstract
CONTEXT: Many biomarkers are proposed in highly cited studies as determinants of disease risk, prognosis, or response to treatment, but few eventually transform clinical practice.

OBJECTIVE: To examine whether the magnitude of the effect sizes of biomarkers proposed in highly cited studies is accurate or overestimated.

DATA SOURCES: We searched ISI Web of Science and MEDLINE until December 2010.

STUDY SELECTION: We included biomarker studies that had a relative risk presented in their abstract. Eligible articles were those that had received more than 400 citations in the ISI Web of Science and that had been published in any of 24 highly cited biomedical journals. We also searched MEDLINE for subsequent meta-analyses on the same associations (same biomarker and same outcome).

DATA EXTRACTION: In the highly cited studies, data extraction was focused on the disease/outcome, biomarker under study, and first reported relative risk in the abstract. From each meta-analysis, we extracted the overall relative risk and the relative risk in the largest study. Data extraction was performed independently by 2 investigators.

RESULTS: We evaluated 35 highly cited associations. For 30 of the 35 (86%), the highly cited studies had a stronger effect estimate than the largest study; for 3 the largest study was also the highly cited study; and only twice was the effect size estimate stronger in the largest than in the highly cited study. For 29 of the 35 (83%) highly cited studies, the corresponding meta-analysis found a smaller effect estimate. Only 15 of the associations were nominally statistically significant based on the largest studies, and of those only 7 had a relative risk point estimate greater than 1.37.

CONCLUSION: Highly cited biomarker studies often report larger effect estimates for postulated associations than are reported in subsequent meta-analyses evaluating the same associations.

Comment in
JAMA. 2011 Jun 1;305(21):2229-30.
PMID:21632484[PubMed - in process]

 

[Esther12]

Thanks.

Reminds me of the "CFS = Depression" meme which is still going strong amongst doctors, despite the fact that the researchers like Wessely who had promoted it have long since abandoned it. (Isn't there some Wessely quote like: "Whatever's wrong with these patients, it's not depression.")

It's a pain.

 

[Snow Leopard]

Ah, John P. A. Ioannidis of "Why Most Published Research Findings Are False" fame.
http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124


"Comparison of Effect Sizes Associated With Biomarkers Reported in Highly Cited Individual Articles and in Subsequent Meta-analyses":
http://jama.ama-assn.org/content/305/21/2200.short

The whole scenario of the relative risks being magnitudes of order in the highly cited papers (published in top journals), compared to the subsequent meta-analyses is a clear result of publication/citation biases.