Dissemination and publication of research findings: an updated review of related biases
Open accessAbstract
OBJECTIVES: To identify and appraise empirical studies on publication and related biases published since 1998; to assess methods to deal with publication and related biases; and to examine, in a random sample of published systematic reviews, measures taken to prevent, reduce and detect dissemination bias. DATA SOURCES: The main literature search, in August 2008, covered the Cochrane Methodology Register Database, MEDLINE, EMBASE, AMED and CINAHL. In May 2009, PubMed, PsycINFO and OpenSIGLE were also searched. Reference lists of retrieved studies were also examined. REVIEW METHODS: In Part I, studies were classified as evidence or method studies and data were extracted according to types of dissemination bias or methods for dealing with it. Evidence from empirical studies was summarised narratively. In Part II, 300 systematic reviews were randomly selected from MEDLINE and the methods used to deal with publication and related biases were assessed. RESULTS: Studies with significant or positive results were more likely to be published than those with non-significant or negative results, thereby confirming findings from a previous HTA report. There was convincing evidence that outcome reporting bias exists and has an impact on the pooled summary in systematic reviews. Studies with significant results tended to be published earlier than studies with non-significant results, and empirical evidence suggests that published studies tended to report a greater treatment effect than those from the grey literature. Exclusion of non-English-language studies appeared to result in a high risk of bias in some areas of research such as complementary and alternative medicine. In a few cases, publication and related biases had a potentially detrimental impact on patients or resource use. Publication bias can be prevented before a literature review (e.g. by prospective registration of trials), or detected during a literature review (e.g. by locating unpublished studies, funnel plot and related tests, sensitivity analysis modelling), or its impact can be minimised after a literature review (e.g. by confirmatory large-scale trials, updating the systematic review). The interpretation of funnel plot and related statistical tests, often used to assess publication bias, was often too simplistic and likely misleading. More sophisticated modelling methods have not been widely used. Compared with systematic reviews published in 1996, recent reviews of health-care interventions were more likely to locate and include non-English-language studies and grey literature or unpublished studies, and to test for publication bias. CONCLUSIONS: Dissemination of research findings is likely to be a biased process, although the actual impact of such bias depends on specific circumstances. The prospective registration of clinical trials and the endorsement of reporting guidelines may reduce research dissemination bias in clinical research. In systematic reviews, measures can be taken to minimise the impact of dissemination bias by systematically searching for and including relevant studies that are difficult to access. Statistical methods can be useful for sensitivity analyses. Further research is needed to develop methods for qualitatively assessing the risk of publication bias in systematic reviews, and to evaluate the effect of prospective registration of studies, open access policy and improved publication guidelines.
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If authors fail to report adverse events or side effects in their abstracts, this will mislead the clinical decision of physicians and may even cause harm to patients.
“If authors fail to report adverse events or side effects in their abstracts, this will mislead the clinical decision of the physicians and may even cause harm to patients”
From Evaluation of reporting quality of abstracts of randomized controlled trials regarding patients with COVID-19 using the CONSORT statement for abstracts (2022) · cites this paper as Song et al., 2010
The “Other evidence” section reports that published articles underreported adverse effects, and the “Clinical trials” section says publication bias could lead patients to receive a harmful treatment. Neither passage specifically addresses omissions from article abstracts or establishes that such omissions will mislead physicians’ clinical decisions; the paper describes patient harm as a possibility. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAlthough peer reviewers and journal editors can be biased in favour of positive findings, lack of publication seems primarily due to trial investigators or sponsors failing to submit negative or null results, rather than journals rejecting them.
“Although peer reviewers and journal editors can be biased in favour of positive findings,119 lack of publication seems to be primarily due to trial investigators or sponsors failing to submit negative or null results, rather than journals rejecting them.114 120”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [114]
Trials with statistically non-significant results are more prone to non-publication or long delay to publication (publication bias).
“Despite providing important information relevant to patients, trials with statistically non-significant results are more prone to non-publication,114 or, when published, to a long delay to publication114 115 116—often referred to as publication bias.”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [114]
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Association Between Bone Density and Fractures in Children: A Systematic Review and Meta-analysis
Clark, E., Tobias, J. H., & Ness, A. (2006) · PEDIATRICS · doi:10.1542/peds.2005-1404 · Cited by 234 · see citing works
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Issues in the Registration of Clinical Trials
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Negative results are disappearing from most disciplines and countries
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A call for transparent reporting to optimize the predictive value of preclinical research
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Beyond PSA: The Next Generation of Prostate Cancer Biomarkers
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- Authors
- Song, F., Parekh, Hooper, L., Loke, Y., Ryder, J., Sutton, A., Hing, C., Kwok, C., Pang, C., & Harvey, I.
- Affiliations
- University of East Anglia (GB)
- Watford General Hospital (GB)
- Corresponding author
- Song, F
- Venue
- Health Technology Assessment
- Published
- 2010-02
- Volume
- 14
- Issue
- 8
- Pages
- iii, ix-xi, 1
- ISSN
- 1366-5278 (ISSN-L)
- 2046-4924
- OpenAlex
- W1994742944
- Cited by
- 1,039
- References
- 610
- Citations by year
- 2026: 69 · 2025: 61 · 2024: 59 · 2023: 61 · 2022: 62 · 2021: 70 · 2020: 90 · 2019: 61 · 2018: 61 · 2017: 64 · 2016: 70 · 2015: 83 · 2014: 84 · 2013: 59 · 2012: 43
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Health Technology Assessment (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Health Technology Assessment programme (06/92/02)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- publication bias, reporting bias, selective reporting, grey literature, clinical trial registration, funnel plot, sensitivity analysis, reporting guidelines, complementary and alternative medicine, research integrity
- MeSH terms
- Humans, Review Literature as Topic, Bias, Publication Bias, Evidence-Based Medicine, Information Dissemination, Biomedical Research
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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