Journal of General Internal Medicine

, Volume 33, Issue 8, pp 1260–1267 | Cite as

Empirical Comparison of Publication Bias Tests in Meta-Analysis

  • Lifeng Lin
  • Haitao Chu
  • Mohammad Hassan Murad
  • Chuan Hong
  • Zhiyong Qu
  • Stephen R. Cole
  • Yong Chen
Original Research



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.


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.


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).


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.


Cochrane Library funnel plot meta-analysis publication bias statistical test 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Supplementary material

11606_2018_4425_MOESM1_ESM.pdf (1.1 mb)
ESM 1 (PDF 1104 kb).


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Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Lifeng Lin
    • 1
  • Haitao Chu
    • 2
  • Mohammad Hassan Murad
    • 3
  • Chuan Hong
    • 4
  • Zhiyong Qu
    • 5
  • Stephen R. Cole
    • 6
  • Yong Chen
    • 7
  1. 1.Department of StatisticsFlorida State UniversityTallahasseeUSA
  2. 2.Division of Biostatistics, School of Public HealthUniversity of MinnesotaMinneapolisUSA
  3. 3.Evidence-Based Practice CenterMayo ClinicRochesterUSA
  4. 4.Department of BiostatisticsHarvard School of Public HealthBostonUSA
  5. 5.School of Social Development and Public PolicyBeijing Normal UniversityBeijingChina
  6. 6.Department of EpidemiologyUNC Gillings School of Global Public HealthChapel HillUSA
  7. 7.Department of Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaUSA

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