Evidence map›Paper›PMID 34130658›Full record

ArticleBMC medical research methodology2021

Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study.

Michael Geissbühler, Cesar A Hincapié, Soheila Aghlmandi, Marcel Zwahlen, Peter Jüni, Bruno R da Costa

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Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 55 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 55 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

94 citing papers in PubMed, 55 syntheses or guidelines pooled it.

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  11. Beyond [BMC cancer · 2026
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34 more citing papers are in PubMed but not listed here.

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

6 authors.

Michael Geissbühler *Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Cesar A Hincapié *Department of Chiropractic Medicine, Faculty of Medicine, Balgrist University Hospital and University of Zurich, Zurich, Switzerland.
Soheila AghlmandiInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Marcel ZwahlenInstitute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Peter Jüni *Applied Health Research Centre (AHRC), Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Canada. peter.juni@utoronto.ca.
Bruno R da Costa *Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012.

methodsA meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012.

resultsOur search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls.

conclusionThe majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Indexed as

PublicationsBiasEpidemiologic StudiesHumansRegression AnalysisEpidemiologic methodsMeta-analysisMeta-regressionMethodological pitfalls

Identifiers

PMID34130658
PMCPMC8207572

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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.