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Cited by 373Meta-analysis and systematic reviewsStatistical Methods in Epidemiologyscientometrics and bibliometrics research

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Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study

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Moreno, Santiago G, Sutton, Alex J, Ades, AE, Stanley, Tom D, Abrams, Keith R, Peters, Jaime L, Cooper, Nicola J·2009·BMC Medical Research Methodology
DOI 10.1186/1471-2288-9-2PMC PMC2649158
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Abstract

BACKGROUND: In meta-analysis, the presence of funnel plot asymmetry is attributed to publication or other small-study effects, which causes larger effects to be observed in the smaller studies. This issue potentially mean inappropriate conclusions are drawn from a meta-analysis. If meta-analysis is to be used to inform decision-making, a reliable way to adjust pooled estimates for potential funnel plot asymmetry is required. METHODS: A comprehensive simulation study is presented to assess the performance of different adjustment methods including the novel application of several regression-based methods (which are commonly applied to detect publication bias rather than adjust for it) and the popular Trim & Fill algorithm. Meta-analyses with binary outcomes, analysed on the log odds ratio scale, were simulated by considering scenarios with and without i) publication bias and; ii) heterogeneity. Publication bias was induced through two underlying mechanisms assuming the probability of publication depends on i) the study effect size; or ii) the p-value. RESULTS: The performance of all methods tended to worsen as unexplained heterogeneity increased and the number of studies in the meta-analysis decreased. Applying the methods conditional on an initial test for the presence of funnel plot asymmetry generally provided poorer performance than the unconditional use of the adjustment method. Several of the regression based methods consistently outperformed the Trim & Fill estimators. CONCLUSION: Regression-based adjustments for publication bias and other small study effects are easy to conduct and outperformed more established methods over a wide range of simulation scenarios.

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Figure 1 Regression line and standard meta-analysis on a funnel plot of simulated asymmetrical data.
Figure 1 Regression line and standard meta-analysis on a funnel plot of simulated asymmetrical data.CC BY · PubMed Central
Figure 2 Schematic outlining the meta-analysis scenarios simulated. 5000 meta-analysis datasets for each combination of the 3 underlying odds ratios, 4 different numbers of trials in the meta-analysis, 4 levels of heterogeneity, and 5 PB situations (240 scenarios in total) were generated.
Figure 2 Schematic outlining the meta-analysis scenarios simulated. 5000 meta-analysis datasets for each combination of the 3 underlying odds ratios, 4 different numbers of trials in the meta-analysis, 4 levels of heterogeneity, and 5 PB situations (240 scenarios in total) were generated.CC BY · PubMed Central
Figure 3 Measures of absolute bias, coverage probabilities, MSE and precision of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 1 (lnOR = 0) and no PB alongside increasing levels of heterogeneity (PB situation 1).
Figure 3 Measures of absolute bias, coverage probabilities, MSE and precision of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 1 (lnOR = 0) and no PB alongside increasing levels of heterogeneity (PB situation 1).CC BY · PubMed Central
Figure 4 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 3 (lnOR = 1.1) and no PB alongside increasing levels of heterogeneity (PB situation 1).
Figure 4 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 3 (lnOR = 1.1) and no PB alongside increasing levels of heterogeneity (PB situation 1).CC BY · PubMed Central
Figure 5 Measures of absolute bias, coverage probabilities, MSE and precision of the predicted effect for homogeneous meta-analyses and underlying OR of 1 (lnOR = 0) and no PB alongside increasing meta-analysis sizes (PB situation 1).
Figure 5 Measures of absolute bias, coverage probabilities, MSE and precision of the predicted effect for homogeneous meta-analyses and underlying OR of 1 (lnOR = 0) and no PB alongside increasing meta-analysis sizes (PB situation 1).CC BY · PubMed Central
Figure 6 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 1 (lnOR = 0) and severe PB induced by p-value alongside increasing heterogeneity (PB situation 2).
Figure 6 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR of 1 (lnOR = 0) and severe PB induced by p-value alongside increasing heterogeneity (PB situation 2).CC BY · PubMed Central
Figure 7 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR = 1.5(lnOR = 0.4) and severe PB induced by p-value alongside increasing heterogeneity (PB situation 2).
Figure 7 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR = 1.5(lnOR = 0.4) and severe PB induced by p-value alongside increasing heterogeneity (PB situation 2).CC BY · PubMed Central
Figure 8 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR = 1 (lnOR = 0) and severe PB induced by effect size alongside increasing heterogeneity (PB situation 4).
Figure 8 Measures of absolute bias, coverage probabilities, MSE and variance of the predicted effect for meta-analyses simulated to have 30 studies, an underlying OR = 1 (lnOR = 0) and severe PB induced by effect size alongside increasing heterogeneity (PB situation 4).CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Peters JL, Sutton AJ, Jones DR, Abrams KR: Performance of Tests and Adjustments for Publication Bias in the Presence of Heterogeneity. Technical report 05-01. 2005, Department of Health Sciences, University of Leicester
  2. Stata: Stata Statistical Software: Release 9.2. College Station, Texas. 2008

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Metadata

Authors
Moreno, S. G., Sutton, A. J., Ades, A., Stanley, T. D., Abrams, K. R., Peters, J. L., & Cooper, N. J.
Affiliations
  • University of Leicester (GB)
  • Hendrix College (US)
Corresponding author
Moreno, Santiago G
ORCID
Venue
BMC Medical Research Methodology
Publisher
Springer Science and Business Media LLC
Published
2009-01-12
Volume
9
Issue
1
Pages
2-2
ISSN
1471-2288 (ISSN-L)
DOI
10.1186/1471-2288-9-2
PMC
PMC2649158
OpenAlex
W1970128129
Cited by
373
References
44
Citations by year
2026: 9 · 2025: 11 · 2024: 11 · 2023: 11 · 2022: 31 · 2021: 28 · 2020: 26 · 2019: 21 · 2018: 39 · 2017: 31 · 2016: 18 · 2015: 26 · 2014: 24 · 2013: 26 · 2012: 19
FWCI
24.04
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Research Methodology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Own your potential (DEAKIN) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
Keywords
publication bias, small-study effects, trim and fill, funnel plot asymmetry, meta-analysis, heterogeneity, binary outcomes, odds ratio, simulation study
MeSH terms
Computer Simulation, Humans, Regression Analysis, Meta-Analysis as Topic, Odds Ratio, Randomized Controlled Trials as Topic, Publication Bias

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