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A Note on Cherry-Picking in Meta-Analyses.

Daisuke Yoneoka1, Bastian Rieck2

  • 1Center for Surveillance, Immunization, and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo 162-8640, Japan.

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Summary
This summary is machine-generated.

Researchers can manipulate meta-analysis results by selectively including studies, potentially creating false treatment effects. This selection bias can occur even with standard meta-analysis methods, impacting study reliability.

Keywords:
adversarial meta-analysischerry-picking studiesinclusion/exclusion criteriameta-analysisselection bias

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Area of Science:

  • Medical research methodology
  • Biostatistics
  • Evidence-based medicine

Background:

  • Meta-analyses are crucial for synthesizing research findings.
  • Selection bias, or cherry-picking, can compromise the integrity of meta-analyses.
  • The potential for bias in meta-analysis methodology requires thorough investigation.

Purpose of the Study:

  • To theoretically investigate selection bias in meta-analyses.
  • To demonstrate how arbitrary inclusion/exclusion criteria can lead to desired results.
  • To evaluate the susceptibility of standard meta-analysis methods to cherry-picking.

Main Methods:

  • Theoretical modeling of selection bias in meta-analysis.
  • Extensive simulation experiments to analyze theoretical findings.
  • Evaluation using practical clinical examples.

Main Results:

  • Meta-analysts can achieve statistically significant or non-significant results regardless of true treatment effects.
  • This manipulation is possible when a sufficient number of studies are available.
  • Numerical evaluations confirm that standard meta-analysis methods are vulnerable to cherry-picking.

Conclusions:

  • Selection bias poses a significant threat to the validity of meta-analyses.
  • Standard meta-analysis techniques may not inherently prevent biased outcomes.
  • Researchers must be vigilant against cherry-picking to ensure reliable scientific evidence.