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dc.contributor.authorDoi, Suhail AR
dc.contributor.authorFuruya-Kanamori, Luis
dc.contributor.authorThalib, Lukman
dc.contributor.authorBarendregt, Jan J
dc.date.accessioned2021-11-07T04:42:20Z
dc.date.available2021-11-07T04:42:20Z
dc.date.issued2017
dc.identifier.issn1744-1609
dc.identifier.doi10.1097/XEB.0000000000000125
dc.identifier.urihttp://hdl.handle.net/10072/409878
dc.description.abstractEach year up to 20000 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 The fixed-effect method has largely been replaced by the random-effects method as heterogeneity of study effects led to poor error estimation.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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherWolters Kluwer Health
dc.relation.ispartofpagefrom152
dc.relation.ispartofpageto160
dc.relation.ispartofissue4
dc.relation.ispartofjournalInternational Journal of Evidence-Based Healthcare
dc.relation.ispartofvolume15
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchNursing
dc.subject.fieldofresearchHealth services and systems
dc.subject.fieldofresearchPublic health
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode4205
dc.subject.fieldofresearchcode4203
dc.subject.fieldofresearchcode4206
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsHealth Care Sciences & Services
dc.subject.keywordsGeneral & Internal Medicine
dc.titleMeta-analysis in evidence-based healthcare: A paradigm shift away from random effects is overdue
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationDoi, SAR; Furuya-Kanamori, L; Thalib, L; Barendregt, JJ, Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue, International Journal of Evidence-Based Healthcare, 2017, 15 (4), pp. 152-160
dc.date.updated2021-11-07T04:35:48Z
gro.hasfulltextNo Full Text
gro.griffith.authorThalib, Lukman


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