A Kinked Meta-Regression Model for Publication Bias Correction

Abstract

Publication selection bias, i.e. the selective reporting of statistically significant results, is a serious problem for any empirical research. As consequence, the estimated effects in the literature can appear considerably larger/smaller than they are and more specifically meta-estimated effect size can be severely biased. Recently, several methods of publication correction have been proposed. None of these methods, however, works reasonably well in general. In particular, PET performs well if the true e?ect is zero, PEESE only works well for large true e?ects, and Top 10 also works well for large true, but is ad-hoc and ine?cient. We propose a novel publication bias method that works well across a wide range of e?ect sizes, publication bias incidences, and meta-sample sizes. It also outperforms a combination of PET and PEESE in a variety of scenarios. Extensive simulations illustrate the gains in terms of efficiency as well as bias.