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P-Hacking in Meta-Analyses: A Formalization and New Meta-Analytic Methods

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  • Author(s): Maya B. Mathur (ORCID Maya B. Mathur (ORCID 0000-0001-6698-2607)
  • Language:
    English
  • Source:
    Research Synthesis Methods. 2024 15(3):483-499.
  • Publication Date:
    2024
  • Document Type:
    Journal Articles
    Reports - Evaluative
  • Additional Information
    • Availability:
      Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
    • Peer Reviewed:
      Y
    • Source:
      17
    • Sponsoring Agency:
      National Institutes of Health (NIH) (DHHS)
    • Contract Number:
      P30CA124435
      P30DK116074
      R01LM013866
      UL1TR003142
    • Subject Terms:
      Meta Analysis; Research Reports; Research Methodology; Research Problems; Programming Languages; Web Sites; Robustness (Statistics)
    • Accession Number:
      10.1002/jrsm.1701
    • ISSN:
      1759-2879
      1759-2887
    • Abstract:
      As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators "p-hack" results "within" their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting "right-truncated meta-analysis" (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS.
    • Abstract:
      As Provided
    • Publication Date:
      2024
    • Accession Number:
      EJ1421897