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Redundant meta-analyses are common in genetic epidemiology. | LitMetric

Redundant meta-analyses are common in genetic epidemiology.

J Clin Epidemiol

Department of Health Research and Policy, Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University School of Humanities and Science, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA.

Published: November 2020

Objectives: The massive growth in the publication of meta-analyses may cause redundancy and wasted efforts. We performed a metaepidemiologic study to evaluate the extent of potential redundancy in published meta-analyses in genetic epidemiology.

Study Design And Setting: Using a sample of 38 index meta-analyses of genetic associations published in 2010, we retrieved additional meta-analyses that evaluated identical associations (same genetic variant and phenotype) using the Human Genome Epidemiology (HuGE) Navigator and PubMed databases. We analyzed the frequency of potential duplication and examined whether subsequent meta-analyses cited previous meta-analyses on the exact same association.

Results: Based on 38 index meta-analyses, we retrieved a total of 99 duplicate meta-analyses. Only 12 (32%) of the index meta-analyses were unambiguously unique. We found a mean of 2.6 duplicates and a median of 2 duplicates per meta-analysis. In case studies, only 29-54% of previously published meta-analyses were cited by subsequent ones.

Conclusion: These results suggest that duplication is common in meta-analyses of genetic associations.

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Source
http://dx.doi.org/10.1016/j.jclinepi.2020.05.035DOI Listing

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