Meta-analysis and publication bias: How well does the FAT-PET-PEESE procedure work?

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  • Author(s): Alinaghi N;Alinaghi N; Reed WR; Reed WR
  • Source:
    Research synthesis methods [Res Synth Methods] 2018 Jun; Vol. 9 (2), pp. 285-311. Date of Electronic Publication: 2018 May 16.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Wiley Blackwell Country of Publication: England NLM ID: 101543738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-2887 (Electronic) Linking ISSN: 17592879 NLM ISO Abbreviation: Res Synth Methods Subsets: MEDLINE
    • Publication Information:
      Publication: : Chichester : Wiley Blackwell
      Original Publication: Malden, MA : John Wiley & Sons, 2010-
    • Subject Terms:
      Meta-Analysis as Topic* ; Models, Statistical* ; Publication Bias*; Algorithms ; Commerce ; Computer Simulation ; Economics ; Humans ; Regression Analysis ; Reproducibility of Results ; Statistics as Topic ; Time Factors
    • Abstract:
      This paper studies the performance of the FAT-PET-PEESE (FPP) procedure, a commonly employed approach for addressing publication bias in the economics and business meta-analysis literature. The FPP procedure is generally used for 3 purposes: (1) to test whether a sample of estimates suffers from publication bias, (2) to test whether the estimates indicate that the effect of interest is statistically different from zero, and (3) to obtain an estimate of the mean true effect. Our findings indicate that the FPP procedure performs well in the basic but unrealistic environment of fixed effects, where all estimates are assumed to derive from a single population value and sampling error is the only reason for why studies produce different estimates. However, when we study its performance in more realistic data environments, where there is heterogeneity in the population effects across and within studies, the FPP procedure becomes unreliable for the first 2 purposes and is less efficient than other estimators when estimating overall mean effect. Further, hypothesis tests about the mean true effect are frequently unreliable. We corroborate our findings by recreating the simulation framework of Stanley and Doucouliagos (2017) and repeat our tests using their framework.
      (Copyright © 2018 John Wiley & Sons, Ltd.)
    • Contributed Indexing:
      Keywords: Monte Carlo; Precision Effect Estimate with Standard Error (PEESE); funnel asymmetry test (FAT); meta-analysis; publication bias; simulations
    • Publication Date:
      Date Created: 20180314 Date Completed: 20181030 Latest Revision: 20220311
    • Publication Date:
      20221216
    • Accession Number:
      10.1002/jrsm.1298
    • Accession Number:
      29532634