Pedro Bom (Universidad de Deusto) and Heiko Rachinger

A Kinked Meta-Regression Model for Publication Bias Correction

For any information please contact J. Vazquez.

Hour: 14.00

Room: TBA


Evidence of publication bias is mounting in virtually all fields of empirical research. Publication bias distorts the available empirical evidence and misinforms policymaking. This paper proposes a novel method of publication bias correction in meta-analysis, the Endogenous Kink (EK) meta-regression model. The EK method fits a piecewise linear meta-regression of the primary estimates on their standard errors, with a kink at the cutoff value of the standard error below which publication selection is unlikely. We provide a simple method of endogenously determining this cutoff value as a function of a first-guess estimate of the true effect and an assumed threshold of statistical significance. Our Monte Carlo simulations show that EK is in general less biased and more efficient that other related regression-based methods of publication bias correction. The main advantage of EK over competing methods lies on its greater versatility across a wide range of true effects.


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