Quantifying Publication Bias in Meta-Analysis
By Lifeng Lin, Haitao Chu. Biometrics, 2017
1,402 citations recorded by the source corpus.
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.
Subfields: Public Health, Environmental and Occupational Health, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/939f713c00383b717a1aa06620dddafe00f5964d
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Citations in the corpus
- Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “The primary goal of our analysis is to understand the content of, and to critically analyse, existing abusive content training datasets, rather than to conduct a meta-analysis [44].”
- The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses (2019, background): “A positive intercept from Egger regression indicates the meta-analysis result tend to be biased toward the right side of the funnel plot, and the missing studies are in the negative direction.([11]) In contrast, a negative intercept implies bias toward the left side, and the missing studies are like”