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A note on cherry-picking in meta-analyses.
Entropy 25:691 (2023)
We study selection bias in meta-analyses by assuming the presence of researchers (meta-analysts) who intentionally or unintentionally cherry-pick a subset of studies by defining arbitrary inclusion and/or exclusion criteria that will lead to their desired results. When the number of studies is sufficiently large, we theoretically show that a meta-analysts might falsely obtain (non)significant overall treatment effects, regardless of the actual effectiveness of a treatment. We analyze all theoretical findings based on extensive simulation experiments and practical clinical examples. Numerical evaluations demonstrate that the standard method for meta-analyses has the potential to be cherry-picked.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Adversarial Meta-analysis ; Cherry-picking Studies ; Inclusion/exclusion Criteria ; Meta-analysis ; Selection Bias
Language
english
Publication Year
2023
HGF-reported in Year
2023
e-ISSN
1099-4300
Journal
Entropy
Quellenangaben
Volume: 25,
Issue: 4,
Article Number: 691
Publisher
MDPI
Reviewing status
Peer reviewed
Institute(s)
Human-Centered AI (HCA)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-540003-001
Scopus ID
85153856450
PubMed ID
37190479
Erfassungsdatum
2023-10-06