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Sensitivity analysis for publication bias in meta-analyses.

Maya B Mathur1, Tyler J VanderWeele2

  • 1Stanford University Palo Alto USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
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Summary

This study introduces sensitivity analyses to assess publication bias in meta-analyses. It quantifies how publication bias, favoring significant results, might affect study outcomes and provides tools to evaluate its impact.

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File drawerMeta‐analysisPublication biasSensitivity analysis

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Area of Science:

  • Meta-analysis
  • Biostatistics
  • Scientific publishing

Background:

  • Publication bias, where statistically significant results are more likely published than non-significant ones, is a pervasive issue in meta-analyses.
  • Existing methods often struggle to quantify the potential impact of this bias on overall findings.
  • The unknown ratio (η) of publication likelihood between significant and non-significant studies complicates bias assessment.

Purpose of the Study:

  • To develop novel sensitivity analyses for quantifying publication bias in meta-analyses.
  • To provide methods that accommodate selection based on statistical significance and standard error.
  • To enable researchers to make quantitative statements about the potential impact of publication bias on meta-analytic results.

Main Methods:

  • Utilized inverse probability weighting and robust estimation techniques.
  • Developed methods to accommodate non-normal population effects, small meta-analyses, and data clustering.
  • Proposed a worst-case scenario analysis using only negative and non-significant studies.

Main Results:

  • Sensitivity analyses allow statements like 'significant results must be at least 30-fold more likely published to shift the estimate to the null'.
  • Methods can assess bias impact on point estimates, non-null values, and confidence intervals.
  • A worst-case analysis can sometimes indicate that publication bias cannot explain away observed results.

Conclusions:

  • The proposed sensitivity analyses offer a robust framework for evaluating publication bias in meta-analyses.
  • Empirical benchmarks for plausible bias ratios (η) are provided to aid interpretation across disciplines.
  • An R package, PublicationBias, is available to implement these methods.