Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression

TL;DR

This paper enhances likelihood-based inference in random-effects meta-analysis and meta-regression by reducing bias in variance estimation, leading to more accurate conclusions especially with few studies.

Contribution

It introduces a bias-reduction methodology for the maximum likelihood estimator of the variance component in meta-analysis and meta-regression.

Findings

Bias reduction improves inference accuracy

Method performs well with small to moderate study numbers

Enhances reliability of meta-analytic conclusions

Abstract

Random-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component can also substantially improve inference about the mean effect size. The results are derived for the more general framework of random-effects meta-regression, which allows the mean effect size to vary with study-specific covariates.