Median bias reduction in random-effects meta-analysis and meta-regression

TL;DR

This paper introduces a bias reduction method for variance estimation in random-effects meta-analysis and meta-regression, improving inference especially with small to moderate study numbers.

Contribution

It extends bias reduction techniques to random-effects meta-regression, enhancing the accuracy of mean effect size inference.

Findings

Bias reduction improves variance estimates in small samples.

Enhanced inference accuracy for mean effects in meta-regression.

Method applicable to study-specific covariate models.

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.