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Meta-regression approximations to reduce publication selection bias.

T D Stanley1, Hristos Doucouliagos2

  • 1Economics, Hendrix College, 1600 Washington St., Conway, AR, 72032, USA.

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Publication selection bias in empirical sciences can be reduced using meta-regression approximations. A new method, precision-effect estimate with standard error (PEESE), shows the least bias and outperforms conventional estimators.

Keywords:
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Area of Science:

  • Empirical sciences
  • Meta-analysis
  • Biostatistics

Background:

  • Publication selection bias challenges the integrity of empirical research.
  • Existing meta-analysis methods may not fully address this bias.

Purpose of the Study:

  • To derive and evaluate meta-regression approximations for reducing publication selection bias.
  • To introduce a novel estimator that combines precision-effect estimate with standard error (PEESE) and Egger regression.

Main Methods:

  • Utilized Taylor polynomial approximations to the conditional mean of a truncated distribution.
  • Developed and simulated a quadratic approximation (PEESE) and a hybrid estimator.
  • Applied methods to policy-relevant research areas.

Main Results:

  • PEESE demonstrated the smallest bias and mean squared error in most simulations.
  • The hybrid estimator offers a practical solution to publication selection bias.
  • PEESE accommodates systematic heterogeneity and complex publication bias.

Conclusions:

  • Meta-regression approximations, particularly PEESE, offer significant improvements over conventional meta-analysis estimators.
  • The developed methods provide robust tools for addressing publication selection bias.
  • These techniques are applicable to diverse policy-relevant research domains.