Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies

Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies

Abstract Background Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling. Methods...

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Bibliographic Details
Main Authors: Svenja E. Seide, Christian Röver, Tim Friede
Format: Article
Language:English
Published: BMC 2019-01-01
Series:BMC Medical Research Methodology
Subjects:
Random-effects meta-analysis
Normal-normal hierarchical model (NNHM)
Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment
Generalized linear mixed model (GLMM)
Count data
Online Access:http://link.springer.com/article/10.1186/s12874-018-0618-3

Internet

http://link.springer.com/article/10.1186/s12874-018-0618-3

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