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AuthorDoi, Suhail A R
AuthorFuruya-Kanamori, Luis
AuthorThalib, Lukman
AuthorBarendregt, Jan J
Available date2017-12-31T05:22:24Z
Publication Date2017-12-01
Publication NameInternational Journal of Evidence-Based Healthcareen_US
Identifierhttp://dx.doi.org/10.1097/XEB.0000000000000125
CitationDoi, S. A., Furuya-Kanamori, L., Thalib, L., & Barendregt, J. J. (2017). Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue. International journal of evidence-based healthcare, 15(4), 152-160.
ISSN1744-1595
URIhttp://hdl.handle.net/10576/6044
AbstractEach year up to 20 000 systematic reviews and meta-analyses are published whose results influence healthcare decisions, thus making the robustness and reliability of meta-analytic methods one of the world's top clinical and public health priorities. The evidence synthesis makes use of either fixed-effect or random-effects statistical methods. The fixed-effect method has largely been replaced by the random-effects method as heterogeneity of study effects led to poor error estimation. However, despite the widespread use and acceptance of the random-effects method to correct this, it too remains unsatisfactory and continues to suffer from defective error estimation, posing a serious threat to decision-making in evidence-based clinical and public health practice. We discuss here the problem with the random-effects approach and demonstrate that there exist better estimators under the fixed-effect model framework that can achieve optimal error estimation. We argue for an urgent return to the earlier framework with updates that address these problems and conclude that doing so can markedly improve the reliability of meta-analytical findings and thus decision-making in healthcare.
Languageen
PublisherUniversity of Adelaide, Joanna Briggs Institute
Subjectcoverage
Subjectheterogeneity
Subjectinverse variance
Subjectmeta-analysis
Subjectrandom effects
Subjectsimulation
TitleMeta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue.
TypeArticle
Pagination152-160
Issue Number4
Volume Number15
dc.identifier.essn 1744-1609
workflow.import.source pubmed


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