Evidence map›Paper›PMID 34521659›Full record

ArticleBMJ open2021

Assessing treatment effects and publication bias across different specialties in medicine: a meta-epidemiological study.

Simon Schwab, Giuachin Kreiliger, Leonhard Held

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  3. Publication Bias in Randomized Controlled Trials of Hypospadias Surgical Repair: A Systematic Review.International journal of urology : official journal of the Japanese Urological Association · 2025
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  16. Systematic Review of Cocaine-Treatment Interventions for Black Americans.Clinical psychological science : a journal of the Association for Psychological Science · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Simon SchwabCenter for Reproducible Science & Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland simon.schwab@uzh.ch.ORCID 0000-0002-1588-2689
Giuachin KreiligerMaster Program in Biostatistics, University of Zurich, Zurich, Switzerland.
Leonhard HeldCenter for Reproducible Science & Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the prevalence of statistically significant treatment effects, adverse events and small-study effects (when small studies report more extreme results than large studies) and publication bias (over-reporting of statistically significant results) across medical specialties.

designLarge meta-epidemiological study of treatment effects from the Cochrane Database of Systematic Reviews.

methodsWe investigated outcomes from 57 162 studies from 1922 to 2019, and overall 98 966 meta-analyses and 5534 large meta-analyses (≥10 studies). Egger's and Harbord's tests to detect small-study effects, limit meta-analysis and Copas selection models to bias-adjust effect estimates and generalised linear mixed models were used to analyse one of the largest collections of evidence in medicine.

resultsMedical specialties showed differences in the prevalence of statistically significant results of efficacy and safety outcomes. Treatment effects from primary studies published in high ranking journals were more likely to be statistically significant (OR=1.52; 95% CI 1.32 to 1.75) while randomised controlled trials were less likely to report a statistically significant effect (OR=0.90; 95% CI 0.86 to 0.94). Altogether 19% (95% CI 18% to 20%) of the large meta-analyses showed evidence for small-study effects, but only 3.9% (95% CI 3.4% to 4.4%) showed evidence for publication bias after further assessment of funnel plots. Adjusting treatment effects resulted in overall less evidence for efficacy.

conclusionsThese results suggest that reporting of large treatment effects from small studies may cause greater concern than publication bias. Incentives should be created so that studies of the highest quality become more visible than studies that report more extreme results.

Indexed as

Data ManagementMedicineEpidemiologic StudiesHumansMeta-Analysis as TopicPublication BiasSystematic Reviews as Topicgeneral medicine (see internal medicine)health informaticsstatistics & research methods

Identifiers

PMID34521659
PMCPMC8442042

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.