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Meta-analysis, funnel plots and sensitivity analysis
Open accessAbstract
Publication bias is a major problem, perhaps the major problem, in meta-analysis (or systematic reviews). Small studies are more likely to be published if their results are 'significant' than if their results are negative or inconclusive, and so the studies available for review are biased in favour of those with positive outcomes. Correcting for this bias is not possible without making untestable assumptions. In this paper, a sensitivity analysis is suggested which is based on fitting a model to the funnel plot. Some examples are discussed.
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“The possibility of publication bias was evaluated with funnel plots and Egger's tests.31, 32”
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Metadata
- Authors
- Copas, J.
- Affiliations
- University of Warwick (GB)
- Corresponding author
- Copas, J.
- ORCID
- Copas, J.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-09-01
- Volume
- 1
- Issue
- 3
- Pages
- 247-262
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- OpenAlex
- W2111636520
- Cited by
- 503
- References
- 19
- Citations by year
- 2026: 36 · 2025: 42 · 2024: 32 · 2023: 31 · 2022: 42 · 2021: 42 · 2020: 42 · 2019: 21 · 2018: 19 · 2017: 34 · 2016: 24 · 2015: 23 · 2014: 24 · 2013: 16 · 2012: 12
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Biostatistics (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Biometrics
- 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, sensitivity analysis, meta-analysis, small-study effects, bias correction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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