Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. (8th March 2017)

• Record Type: Journal Article
• Authors: Kosmidis, I.; Guolo, A.; Varin, C.
• Is Part Of: Biometrika. Volume 104:Number 2(2017)
• Page Start: 489
• Page End: 496
• Publication Date: 2017-03-08
• Subjects: Bias reduction -- Heterogeneity -- Meta-analysis -- Penalized likelihood -- Random effect -- Restricted maximum likelihood; Biometry -- Periodicals; 570.1519505
• DOI: 10.1093/biomet/asx001
• Languages: English
• ISSNs: 0006-3444

Abstract

Summary : Random-effects models are frequently used to synthesize 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.