Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/23382
Title: Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study.
Authors: Moreno, Santiago G.
Sutton, Alex J.
Ades, A. E.
Stanley, T. D.
Abrams, K. R.
Peters, J. L.
Cooper, N. J.
First Published: 12-Jan-2009
Publisher: BioMed Central Ltd
Citation: BMC Medical Research Methodology, 2009, 9 : 2
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.
DOI Link: 10.1186/1471-2288-9-2
eISSN: 1471-2288
Links: http://hdl.handle.net/2381/23382
http://www.biomedcentral.com/1471-2288/9/2
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2009 Moreno et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:Published Articles, Dept. of Health Sciences

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