Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression

• 成果类型: Article
• 署名作者: Kosmidis, I.; Guolo, A.; Varin, C.
• 署名单位: University of London; University College London; University of Padua; Universita Ca Foscari Venezia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx001
• 发表日期: 2017
• 页码: 489496
• 关键词: simple confidence-interval; BIAS

摘要

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.