Evidence map›Paper›PMID 35869696›Full record

SynthesisResearch synthesis methods2023

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

František Bartoš, Maximilian Maier, Eric-Jan Wagenmakers, Hristos Doucouliagos, T D Stanley

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Research synthesis methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 23 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 23 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

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

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  14. Individual differences in inattentional blindness.Psychonomic bulletin & review · 2024
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  19. Impact of cardiovascular magnetic resonance-derived right ventricular ejection fraction on adverse outcomes: A robust Bayesian model-averaged meta-analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024
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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

5 authors.

František BartošDepartment of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0002-0018-5573
Maximilian MaierDepartment of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0002-9873-6096
Eric-Jan WagenmakersDepartment of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0003-1596-1034
Hristos DoucouliagosDeakin Laboratory for the Meta-Analysis of Research (DeLMAR), Deakin University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-5269-3556
T D StanleyDeakin Laboratory for the Meta-Analysis of Research (DeLMAR), Deakin University, Melbourne, Australia.ORCID https://orcid.org/0000-0002-3205-1983

Funding

NWO 016.Vici.170.083
6 · The paper itself

Abstract

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

Indexed as

Models, StatisticalBayes TheoremBiasComputer SimulationPublication BiasBayesian model-averagingmeta-analysisPET-PEESEpublication biasselection models

Identifiers

PMID35869696
PMCPMC10087723

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.