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Modelling publication bias in meta-analysis: a review
Abstract
Meta-analysis is now a widely used technique for summarizing evidence from multiple studies. Publication bias, the bias induced by the fact that research with statistically significant results is potentially more likely to be submitted and published than work with null or non-significant results, poses a thereat to the validity of such analyses. The implication of this is that combining only the identified published studies uncritically may lead to an incorrect, usually over optimistic, conclusion. How publication bias should be addressed when carrying out a meta-analysis is currently a hotly debated subject. While statistical methods to test for its presence are starting be used, they do not address the problem of how to proceed if publication bias is suspected. This paper provides a review of methods, which can be employed as a sensitivity analysis to assess the likely impact of publication bias on a meta-analysis. It is hoped that this will raise awareness of such methods, and promote their use and development, as well as provide an agenda for future research.
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“There are two main threats to the validity of meta-analytic results: the systematic omission of studies (e.g., publication bias suppressing studies with null results36,37), and the flexible selection of analysis approaches within each study inflating the rate of false positives (e.g., p-hacking38).”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [36]
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Metadata
- Authors
- Sutton, A. J., Song, F., Gilbody, S. M., & Abrams, K. R.
- Affiliations
- Department of Epidemiology and Public Health, University of Leicester, Leicester, UK,
- University of York (GB)
- University of Leicester (GB)
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2000-10
- Volume
- 9
- Issue
- 5
- Pages
- 421-445
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2009813561
- Cited by
- 288
- References
- 63
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 10 · 2023: 27 · 2022: 15 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 14 · 2017: 25 · 2016: 10 · 2015: 15 · 2014: 14 · 2013: 12 · 2012: 11
- FWCI
- 11.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- 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, meta-analysis, sensitivity analysis, bias correction, statistical significance, negative results, research validity
- MeSH terms
- Bayes Theorem, Data Interpretation, Statistical, United Kingdom, Meta-Analysis as Topic, Models, Statistical, Publication Bias
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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