Log in to MyACC
Comparison of Effect Sizes Associated With Biomarkers Reported in Highly Cited Individual Articles and in Subsequent Meta-Analyses
Study Questions:
How reliable are the effect sizes of biomarkers reported in highly cited studies?
Methods:
Eligible biomarker studies were those that had received more than 400 citations in the ISI Web of Science. A MEDLINE search for subsequent meta-analyses on the same associations (same biomarker and same outcome) was also performed. 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, the overall relative risk and the relative risk in the largest study was extracted.
Results:
Thirty-five highly cited associations were analyzed. For 30 of the 35 (86%), the highly cited studies had a stronger effect estimate than the largest study; for three, 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 seven had a relative risk point estimate greater than 1.37.
Conclusions:
The authors concluded that highly cited biomarker studies often report larger effect estimates for postulated associations than are reported in subsequent meta-analyses evaluating the same associations.
Perspective:
Citation bias for extreme results and failure to replicate initial exciting observations is common in all areas of research. This has been studied and shown for clinical interventions/outcomes, genome-wide association studies, and now biomarkers. There are multiple explanations for this, and a healthy skepticism of the medical literature is needed, especially when the results of a study may impact patient management. Progress in the medical field is much slower than the literature would indicate.
Keywords: Risk, Biomarkers, Cardiology, Bias, Genome-Wide Association Study, Biomedical Research
< Back to Listings
