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Empirical Comparison of Publication Bias Tests in Meta-Analysis

Journal of general internal medicine : JGIM, 2018-08, Vol.33 (8), p.1260-1267 [Peer Reviewed Journal]

Society of General Internal Medicine 2018 ;Journal of General Internal Medicine is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0884-8734 ;EISSN: 1525-1497 ;DOI: 10.1007/s11606-018-4425-7 ;PMID: 29663281

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  • Title:
    Empirical Comparison of Publication Bias Tests in Meta-Analysis
  • Author: Lin, Lifeng ; Chu, Haitao ; Murad, Mohammad Hassan ; Hong, Chuan ; Qu, Zhiyong ; Cole, Stephen R. ; Chen, Yong
  • Subjects: Bias ; Clinical trials ; Decision analysis ; Empirical analysis ; Empirical Research ; Humans ; Internal Medicine ; Medical research ; Medicine ; Medicine & Public Health ; Meta-analysis ; Meta-Analysis as Topic ; Original Research ; Publication Bias - statistics & numerical data ; Regression analysis ; Statistical analysis ; Statistical tests ; Systematic Reviews as Topic
  • Is Part Of: Journal of general internal medicine : JGIM, 2018-08, Vol.33 (8), p.1260-1267
  • Description: ABSTRACT Background Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data. Methods This study compares seven commonly used publication bias tests (i.e., Begg’s rank test, trim-and-fill, Egger’s, Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests) based on 28,655 meta-analyses available in the Cochrane Library. Results Egger’s regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg’s rank test and the trim-and-fill method. The agreement among Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests for binary outcomes was moderately strong (most κ ’s were around 0.6). Tang’s and Deeks’ tests had fairly similar performance ( κ  > 0.9). The agreement among Begg’s rank test, the trim-and-fill method, and Egger’s regression test was weak or moderate ( κ < 0.5). Conclusions Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0884-8734
    EISSN: 1525-1497
    DOI: 10.1007/s11606-018-4425-7
    PMID: 29663281
  • Source: PubMed
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    MEDLINE
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