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dc.contributor.authorPatsopoulos, N. A.en
dc.contributor.authorEvangelou, E.en
dc.contributor.authorIoannidis, J. P.en
dc.date.accessioned2015-11-24T18:35:37Z-
dc.date.available2015-11-24T18:35:37Z-
dc.identifier.issn0300-5771-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/17117-
dc.rightsDefault Licence-
dc.subjectheterogeneityen
dc.subjectsensitivity analysisen
dc.subjectsequential algorithmen
dc.subjectmeta-analysisen
dc.subjectindividual patient dataen
dc.subjectmeta-regressionen
dc.subjectexploring heterogeneityen
dc.subjectgenetic associationen
dc.subjectsystematic reviewsen
dc.subjectclinical-trialsen
dc.subjectlevelen
dc.titleSensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluationen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1093/Ije/Dyn065-
heal.identifier.secondary<Go to ISI>://000259771500031-
heal.identifier.secondaryhttp://ije.oxfordjournals.org/content/37/5/1148.full.pdf-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών και Τεχνολογιών. Τμήμα Βιολογικών Εφαρμογών και Τεχνολογιώνel
heal.publicationDate2008-
heal.abstractBackground Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. Methods We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >= 4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I(2) thresholds were used (50% and 25%). Results Both algorithms have succeeded in achieving the pre-specified final I(2) thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I(2) > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I(2) < 50. Similarly, among meta-analyses with initial I(2) 25, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even < 25%. The number of excluded studies correlated modestly with initial estimated I(2) (correlation coefficients 0.52-0.68 depending on algorithm used). Conclusions The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity.en
heal.journalNameInt J Epidemiolen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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