Kosmidis, I;
Guolo, A;
Varin, C;
(2017)
Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression.
Biometrika
, 104
(2)
pp. 489-496.
10.1093/biomet/asx001.
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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.
| Type: | Article |
|---|---|
| Title: | Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/biomet/asx001 |
| Publisher version: | https://doi.org/10.1093/biomet/asx001 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Bias reduction, Heterogeneity, Meta-analysis, Penalized likelihood, Random effect, Restricted maximum likelihood |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1494926 |
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