A kinked meta-regression model for publication bias correction.

Area of Science:

• Empirical research methodology
• Meta-analysis
• Econometrics

Background:

• Publication bias significantly distorts empirical evidence across all research fields.
• Biased evidence misinforms critical policymaking decisions.
• Existing methods for publication bias correction have limitations.

Purpose of the Study:

• To introduce a novel meta-regression model for publication bias correction.
• To propose the endogenous kink (EK) method as a solution.
• To evaluate the performance of the EK method against existing techniques.

Main Methods:

• Developed the endogenous kink (EK) meta-regression model.
• The EK method employs a piecewise linear meta-regression of primary estimates against their standard errors.
• A novel approach endogenously determines the cutoff value for publication selection likelihood.

Main Results:

• Monte Carlo simulations demonstrate the EK method's effectiveness.
• The EK method shows reduced bias compared to other regression-based correction methods.
• The EK method exhibits greater efficiency in various research conditions.

Conclusions:

• The endogenous kink (EK) model provides a statistically sound and effective approach to correcting publication bias.
• This method enhances the reliability of empirical evidence synthesis.
• The EK method offers a valuable tool for researchers and policymakers seeking accurate evidence.