Kosmidis, Ioannis, Guolo, A. and Varin, Cristiano (2017) Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression. Biometrika, 104 (2). pp. 489-496. doi:10.1093/biomet/asx001

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Abstract

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.

Item Type:	Journal Article
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Multilevel models (Statistics), Mathematical statistics, Meta-analysis, Regression analysis, Asymptotic distribution (Probability theory)
Journal or Publication Title:	Biometrika
Publisher:	Biometrika Trust
ISSN:	0006-3444
Official Date:	1 September 2017
Dates:	Date Event 1 September 2017 Published 1 June 2017 Available 8 March 2017 Accepted
Volume:	104
Number:	2
Page Range:	pp. 489-496
DOI:	10.1093/biomet/asx001
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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