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<p>Publication bias is a major threat to the validity of a meta-analysis, resulting in overestimated effect sizes. We propose a generalization and improvement of the publication bias method <i>p-</i>uniform called <i>p</i>-uniform*. <i>P</i>-uniform* improves upon <i>p</i>-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 sizes, and (iii) enables estimating and testing for the presence of the between-study variance. We compared the statistical properties of <i>p</i>-uniform* with <i>p-</i>uniform, two implementations of the three-parameter selection model (3PSM) approach, and the random-effects model. Statistical properties of <i>p</i>-uniform* and 3PSM were comparable and generally outperformed <i>p-</i>uniform and the random-effects model if publication bias was present. We explain that <i>p-</i>uniform* uses a more parsimonious model than 3PSM and demonstrate that both methods 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. We re-analyze the data of two published meta-analyses using <i>p</i>-uniform, <i>p-</i>uniform*, and 3PSM to illustrate the impact of publication bias on the results. We also offer recommendations for applied researchers, and we share R code in an R package as well as an easy-to-use web application for applying <i>p</i>-uniform*.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月27日

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  • Robbie C. M. van Aert,
  • Marcel A. L. M. van Assen

摘要

Publication bias is a major threat to the validity of a meta-analysis, resulting in overestimated effect sizes. We propose a generalization 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 sizes, 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, two implementations of the three-parameter selection model (3PSM) approach, and the random-effects model. Statistical properties of p-uniform* and 3PSM were comparable and generally outperformed p-uniform and the random-effects model if publication bias was present. We explain that p-uniform* uses a more parsimonious model than 3PSM and demonstrate that both methods 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. We re-analyze the data of two published meta-analyses using p-uniform, p-uniform*, and 3PSM to illustrate the impact of publication bias on the results. We also offer recommendations for applied researchers, and we share R code in an R package as well as an easy-to-use web application for applying p-uniform*.