Evidence map›Paper›PMID 34535060›Full record

SynthesisAnnual review of public health2022

Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations.

Maya B Mathur, Tyler J VanderWeele

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

Synthesis in Annual review of public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 11 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

35 citing papers in PubMed, 11 syntheses or guidelines pooled it.

  1. Predictors of clinical response to biologics in psoriasis: A systematic review and meta-analysis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026
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  12. Soil pH Amelioration Synergizes Carbon Accrual with Yield Gains Under Organic Substitution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

2 authors.

Maya B MathurQuantitative Sciences Unit and Department of Pediatrics, Stanford University, Stanford, California, USA; email: mmathur@stanford.edu.
Tyler J VanderWeeleDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, 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

Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than do earlier methods. We give recommendations for how these newer methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies' risks of bias qualitatively.

Indexed as

BiasHumansbiasconfoundingmeta-analysisobservational studiessensitivity analysis

Identifiers

PMID34535060
PMCPMC8959012

What OpenQuestion holds

Textmetadata
LicenceTDM
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.