Detecting publication bias in random effects meta-analysis: An empirical comparison of statistical methods

Abstract

Publication bias is one threat to validity that researchers conducting meta-analysis studies confront. Two primary goals of this research were to examine the degree to which publication bias impacts the results of a random effects meta-analysis and to investigate the performance of five statistical methods for detecting publication bias in random effects meta-analysis. Specifically, the difference between the population effect size and the estimated meta-analysis effect size, as well as the difference between the population effect size variance and the meta-analysis effect size variance, provided an indication of the impact of publication bias. In addition, the performance of five statistical methods for detecting publication bias (Begg Rank Correlation with sample size, Begg Rank Correlation with variance, Egger Regression, Funnel Plot Regression, and Trim and Fill) were estimated with Type I error rates and statistical power. The overall findings indicate that publication bias notably impacts the meta-analysis effect size and variance estimates. Poor FTSe I error control was exhibited in many conditions by most of the statistical methods. Even when Type I error rates were adequate the power was small, even with larger samples and greater numbers of studies in the meta-analysis.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 22 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.