In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias
Abstract
Objective To assess the utility of funnel plots in assessing publication bias (PB) in meta-analyses of proportion studies. Study design and setting Meta-analysis simulation study and meta-analysis of published literature reporting peri-operative mortality after abdominal aortic aneurysm (AAA) repair. Data for the simulation study were stochastically generated. A literature search of Medline and Embase was performed to identify studies for inclusion in the published literature meta-analyses. Results The simulation study demonstrated that conventionally constructed funnel plots (log odds vs. 1/standard error [1/SE]) for extreme proportional outcomes were asymmetric despite no PB. Alternative funnel plots constructed using study size rather than 1/SE showed no asymmetry for extreme proportional outcomes. When used in meta-analyses of the mortality of AAA repair, these alternative funnel plots highlighted the possibility for conventional funnel plots to demonstrate asymmetry when there was no evidence of PB. Conclusion Conventional funnel plots used to assess for potential PB in meta-analyses are inaccurate for meta-analyses of proportion studies with low proportion outcomes. Funnel plots of study size against log odds may be a more accurate way of assessing for PB in these studies.
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The authors did not assess publication bias because funnel plots have been found to give erroneous results when pooling prevalence data and are not recommended for prevalence effect size.
“In addition, we did not assess the publication bias because funnel plots have been found to give erroneous results when pooling prevalence data and they are not recommended to be used when dealing with prevalence effect size (Hunter et al., 2014).”
From Autism in the context of joint hypermobility, hypermobility spectrum disorders, and Ehlers–Danlos syndromes: A systematic review and prevalence meta-analyses (2025) · cites this paper as Hunter et al., 2014
The usefulness of standard publication bias tests for proportional meta-analyses has been questioned, with funnel plots and statistical tests potentially yielding misleading results.
“the usefulness of standard publication bias tests for proportional meta-analyses has been questioned, with funnel plots and statistical tests potentially yielding misleading results.66”
From Co-infections in people with COVID-19: a systematic review and meta-analysis. (2020) · cites this paper as [66]
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- Authors
- Hunter, J. P., Saratzis, A., Sutton, A. J., Boucher, R. H., Sayers, R. D., & Bown, M. J.
- Affiliations
- Leicester Royal Infirmary (GB)
- University Hospital Coventry (GB)
- University of Leicester (GB)
- Corresponding author
- Hunter, James P.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 67
- Issue
- 8
- Pages
- 897-903
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2136145129
- Cited by
- 786
- References
- 32
- Citations by year
- 2026: 71 · 2025: 93 · 2024: 95 · 2023: 108 · 2022: 119 · 2021: 93 · 2020: 59 · 2019: 56 · 2018: 25 · 2017: 26 · 2016: 22 · 2015: 17 · 2014: 1
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (CL-2014-11-001, MET-12-01-101)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- publication bias, funnel plot, study size, abdominal aortic aneurysm repair, perioperative mortality, funnel plot asymmetry, simulation study
- MeSH terms
- Data Interpretation, Statistical, Humans, Stochastic Processes, Cardiovascular Surgical Procedures, Models, Statistical, Aortic Aneurysm, Abdominal, Publication Bias
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