‘Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression’
本文展示了如何通过减少方差成分最大似然估计的渐近偏差，来改善元分析中平均效应大小的推断，尤其适用于研究数量较少的情况。
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.