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. 2018 Sep;74(3):785-794.
doi: 10.1111/biom.12817. Epub 2017 Nov 15.

Quantifying publication bias in meta-analysis

Lifeng Lin  1 , Haitao Chu  1
Affiliations

Quantifying publication bias in meta-analysis

Lifeng Lin et al. Biometrics. 2018 Sep.

Abstract

Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect size estimate and are usually employed as sensitivity analyses to assess the potential impact of publication bias. Funnel-plot-based methods include visual examination of a funnel plot, regression and rank tests, and the nonparametric trim and fill method. Although these approaches have been widely used in applications, measures for quantifying publication bias are seldom studied in the literature. Such measures can be used as a characteristic of a meta-analysis; also, they permit comparisons of publication biases between different meta-analyses. Egger's regression intercept may be considered as a candidate measure, but it lacks an intuitive interpretation. This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias. This measure describes the asymmetry of the collected studies' distribution. In addition, a new test for publication bias is derived based on the skewness. Large sample properties of the new measure are studied, and its performance is illustrated using simulations and three case studies.

Keywords: Heterogeneity; Meta-analysis; Publication bias; Skewness; Standardized deviate; Statistical power.

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Figures

Figure 1
Figure 1
The funnel plot of a simulated meta-analysis containing 60 studies. The 10 studies with the most negative effect sizes were suppressed due to publication bias, and the remaining 50 studies were “published”.
Figure 2
Figure 2
Contour-enhanced funnel plots of the three actual meta-analyses. The vertical and diagonal dashed lines represent the overall estimated effect size and its 95% confidence limits, respectively, based on the fixed-effect model. The shaded regions represent different significance levels for the effect size.
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