Redundant meta-analyses are common in genetic epidemiology

Matthew K Sigurdson; Muin J Khoury; John P A Ioannidis

doi:10.1016/j.jclinepi.2020.05.035

Redundant meta-analyses are common in genetic epidemiology

J Clin Epidemiol. 2020 Nov:127:40-48. doi: 10.1016/j.jclinepi.2020.05.035. Epub 2020 Jun 12.

Authors

Matthew K Sigurdson¹, Muin J Khoury², John P A Ioannidis³

Affiliations

¹ 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. Electronic address: msigurds@stanford.edu.
² Office of Public Health Genomics, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.
³ 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.

PMID: 32540390
DOI: 10.1016/j.jclinepi.2020.05.035

Abstract

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.

Keywords: Citation analysis; Duplication; Genetic epidemiology; Meta-analysis; Metaresearch; Research efficiency.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Efficiency
Genetic Variation
Genome, Human*
Genotype
Humans
Meta-Analysis as Topic*
Molecular Epidemiology / statistics & numerical data*