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The appropriateness of asymmetry tests for publication bias in meta-analyses: a large survey
Open accessAbstract
BACKGROUND: Statistical tests for funnel-plot asymmetry are common in meta-analyses. Inappropriate application can generate misleading inferences about publication bias. We aimed to measure, in a survey of meta-analyses, how frequently the application of these tests would be not meaningful or inappropriate. METHODS: We evaluated all meta-analyses of binary outcomes with é 3 studies in the Cochrane Database of Systematic Reviews (2003, issue 2). A separate, restricted analysis was confined to the largest meta-analysis in each of the review articles. In each meta-analysis, we assessed whether criteria to apply asymmetry tests were met: no significant heterogeneity, I2 < 50%, é 10 studies (with statistically significant results in at least 1) and ratio of the maximal to minimal variance across studies > 4. We performed a correlation and 2 regression asymmetry tests and evaluated their concordance. Finally, we sampled 60 meta-analyses from print journals in 2005 that cited use of the standard regression test. RESULTS: A total of 366 of 6873 (5%) and 98 of 846 meta-analyses (12%) in the wider and restricted Cochrane data set, respectively, would have qualified for use of asymmetry tests. Asymmetry test results were significant in 7%-18% of the meta-analyses. Concordance between the 3 tests was modest (estimated k 0.33-0.66). Of the 60 journal meta-analyses, 7 (12%) would qualify for asymmetry tests; all 11 claims for identification of publication bias were made in the face of large and significant heterogeneity. INTERPRETATION: Statistical conditions for employing asymmetry tests for publication bias are absent from most meta-analyses; yet, in medical journals these tests are performed often and interpreted erroneously.
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“However, publication bias tests might be problematic for IN-OT studies due to small sample sizes resulting in insufficient statistical power and sample size variability for the tests to yield reliable and significant results (26).”
From Statistical and Methodological Considerations for the Interpretation of Intranasal Oxytocin Studies (2016) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“The small number of studies and presence of significant heterogeneity precluded formal testing for publication bias[35].”
From Daily Sitting Time and All-Cause Mortality: A Meta-Analysis (2013) · cites this paper as [35]
In the citing paper
“Of 60 meta-analyses in 24 articles published in 2005 in which formal assessments were reported, most were based on fewer than ten studies; most displayed statistically significant heterogeneity; and many reviewers misinterpreted the results of the tests employed [123].”
From The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration (2009) · cites this paper as [123]
In the citing paper
“Although evidence that smaller studies had larger estimated effects than large ones may suggest the possibility that the available evidence is biased, misinterpretation of such data is common [123].”
From The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration (2009) · cites this paper as [123]
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Cites
Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
Operating characteristics of a rank correlation test for publication bias
Begg, C. B., & Mazumdar, M. (1994) · Biometrics · doi:10.2307/2533446 · Cited by 16,955
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
Self-citationCochrane Handbook for Systematic Reviews of Interventions
Tarsilla, M. (2010) · Journal of MultiDisciplinary Evaluation · doi:10.56645/jmde.v6i14.284 · Cited by 5,232
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702
Self-citationFunnel plots for detecting bias in meta-analysis
Sterne, J. A. C., & Egger, M. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00377-8 · Cited by 3,779
Publication bias in clinical research
Easterbrook, P. J., Gopalan, R., Berlin, J. A., & Matthews, D. R. (1991) · The Lancet · doi:10.1016/0140-6736(91)90201-y · Cited by 3,109
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Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164
Self-citationLiberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Self-citationLiberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.06.006 · Cited by 10,578
Self-citationLiberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00136 · Cited by 10,349
Self-citationSterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rucker, G., Harbord, R. M., Schmid, C. H., et al. (2011) · BMJ · doi:10.1136/bmj.d4002 · Cited by 7,709
“After reviewing the results of these studies, and based on theoretical considerations, we formulated recommendations on testing for funnel plot asymmetry (box 2). The appendix describes the proposed tests, explains the reasons that some were not recommended, and discusses funnel plots for intervention effects measured…”
MethodSelf-citation
Metadata
- Authors
- Ioannidis, J. P. A., & Trikalinos, T. A.
- Affiliations
- Tufts University (US)
- Venue
- Canadian Medical Association Journal
- Publisher
- CMA Joule Inc.
- Published
- 2007-04-10
- Volume
- 176
- Issue
- 8
- Pages
- 1091-1096
- ISSN
- 0820-3946 (ISSN-L)
- 1488-2329
- PMC
- PMC1839799
- OpenAlex
- W2164093802
- Cited by
- 1,052
- References
- 40
- Citations by year
- 2026: 49 · 2025: 48 · 2024: 64 · 2023: 49 · 2022: 81 · 2021: 73 · 2020: 83 · 2019: 81 · 2018: 73 · 2017: 55 · 2016: 62 · 2015: 64 · 2014: 68 · 2013: 38 · 2012: 39
- FWCI
- 31.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Canadian Medical Association Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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 › Library and Information Sciences › scientometrics and bibliometrics research
- Keywords
- publication bias, funnel plot asymmetry, meta-analysis, heterogeneity, Cochrane Database of Systematic Reviews, binary outcomes, test concordance
- MeSH terms
- Data Interpretation, Statistical, Humans, Periodicals as Topic, Meta-Analysis as Topic, Databases, Bibliographic, Publication Bias
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