Correcting for publication bias in a meta-analysis with the P-uniform* method

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Abstract

Publication bias is a major threat to the validity of a meta-analysis resulting in overestimated effect sizes. We propose an extension and improvement of the publication bias method p-uniform called p-uniform*. P-uniform* improves upon p-uniform in three ways, as it (i) entails a more efficient estimator, (ii) eliminates the overestimation of effect size caused by between-study variance in true effect size, and (iii) enables estimating and testing for the presence of the between-study variance. We compared the statistical properties of p-uniform* with p-uniform, the selection model approach of Hedges (1992), and the random-effects model. Statistical properties of p-uniform* and the selection model approach were comparable and generally outperformed p-uniform and the random-effects model if publication bias was present. We demonstrate that p-uniform* and the selection model approach estimate average effect size and between-study variance rather well with ten or more studies in the meta-analysis when publication bias is not extreme. P-uniform* generally provides more accurate estimates of the between-study variance in meta-analyses containing many studies (e.g., 60 or more) and if publication bias is present. We offer recommendations for applied researchers, provide an R package as well as an easy-to-use web application for applying p-uniform*.
Original languageEnglish
PublisherMetaArXiv Preprints
Number of pages51
DOIs
Publication statusPublished - 2018

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