Evidence map›Paper›PMID 36909879›Full record

ArticleCampbell systematic reviews2022

Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use.

Maximilian Maier, Tyler J VanderWeele, Maya B Mathur

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Campbell systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 13 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

26 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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  11. Does the use of pharmacotherapy interact with the effects of psychotherapy? A meta-analytic review.European psychiatry : the journal of the Association of European Psychiatrists · 2023
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  17. Treating gaps and biases in biodiversity data as a missing data problem.Biological reviews of the Cambridge Philosophical Society · 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.

Maximilian MaierDepartment of Experimental Psychology University College London London UK.
Tyler J VanderWeeleDepartment of Epidemiology Harvard University Cambridge Massachusetts USA.
Maya B MathurQuantitative Sciences Unit, Department of Pediatrics Stanford University Stanford California USA.

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Vittorio Sebastiano · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Theory and methods for mediation and interactionR01CA222147 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL, VANDERWEELE, TYLER · 2018 to 2022
$2.1M
Statistical Methods for Modern Evidence Syntheses with Multiple BiasesR01LM013866 · NLM · STANFORD UNIVERSITY · PI MATHUR, MAYA · 2021 to 2024
$1.3M
NCATS NIH HHS UL1 TR003142NCI NIH HHS P30 CA124435NCI NIH HHS R01 CA222147NIDDK NIH HHS P30 DK116074NLM NIH HHS R01 LM013866
6 · The paper itself

Abstract

In meta-analyses, it is critical to assess the extent to which publication bias might have compromised the results. Classical methods based on the funnel plot, including Egger's test and Trim-and-Fill, have become the de facto default methods to do so, with a large majority of recent meta-analyses in top medical journals (85%) assessing for publication bias exclusively using these methods. However, these classical funnel plot methods have important limitations when used as the sole means of assessing publication bias: they essentially assume that the publication process favors large point estimates for small studies and does not affect the largest studies, and they can perform poorly when effects are heterogeneous. In light of these limitations, we recommend that meta-analyses routinely apply other publication bias methods in addition to or instead of classical funnel plot methods. To this end, we describe how to use and interpret selection models. These methods make the often more realistic assumption that publication bias favors "statistically significant" results, and the methods also directly accommodate effect heterogeneity. Selection models have been established for decades in the statistics literature and are supported by user-friendly software, yet remain rarely reported in many disciplines. We use a previously published meta-analysis to demonstrate that selection models can yield insights that extend beyond those provided by funnel plot methods, suggesting the importance of establishing more comprehensive reporting practices for publication bias assessment.

Identifiers

PMID36909879
PMCPMC9247867

What OpenQuestion holds

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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.