Can trial sequential monitoring boundaries reduce spurious inferences from meta-analyses?

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dc.contributor.authorThorlund, K.en
dc.contributor.authorDevereaux, P. J.en
dc.contributor.authorWetterslev, J.en
dc.contributor.authorGuyatt, G.en
dc.contributor.authorIoannidis, J. P.en
dc.contributor.authorThabane, L.en
dc.contributor.authorGluud, L. L.en
dc.contributor.authorAls-Nielsen, B.en
dc.contributor.authorGluud, C.en
dc.date.accessioned2015-11-24T19:31:55Z
dc.date.available2015-11-24T19:31:55Z
dc.identifier.issn1464-3685-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/23341
dc.rightsDefault Licence-
dc.subjectData Interpretation, Statisticalen
dc.subjectFalse Positive Reactionsen
dc.subjectHumansen
dc.subject*Meta-Analysis as Topicen
dc.subjectRandomized Controlled Trials as Topic/methodsen
dc.subjectResearch Designen
dc.subjectTreatment Outcomeen
dc.titleCan trial sequential monitoring boundaries reduce spurious inferences from meta-analyses?en
heal.abstractBACKGROUND: Results from apparently conclusive meta-analyses may be false. A limited number of events from a few small trials and the associated random error may be under-recognized sources of spurious findings. The information size (IS, i.e. number of participants) required for a reliable and conclusive meta-analysis should be no less rigorous than the sample size of a single, optimally powered randomized clinical trial. If a meta-analysis is conducted before a sufficient IS is reached, it should be evaluated in a manner that accounts for the increased risk that the result might represent a chance finding (i.e. applying trial sequential monitoring boundaries). METHODS: We analysed 33 meta-analyses with a sufficient IS to detect a treatment effect of 15% relative risk reduction (RRR). We successively monitored the results of the meta-analyses by generating interim cumulative meta-analyses after each included trial and evaluated their results using a conventional statistical criterion (alpha = 0.05) and two-sided Lan-DeMets monitoring boundaries. We examined the proportion of false positive results and important inaccuracies in estimates of treatment effects that resulted from the two approaches. RESULTS: Using the random-effects model and final data, 12 of the meta-analyses yielded P > alpha = 0.05, and 21 yielded P </= alpha = 0.05. False positive interim results were observed in 3 out of 12 meta-analyses with P > alpha = 0.05. The monitoring boundaries eliminated all false positives. Important inaccuracies in estimates were observed in 6 out of 21 meta-analyses using the conventional P </= alpha = 0.05 and 0 out of 21 using the monitoring boundaries. CONCLUSIONS: Evaluating statistical inference with trial sequential monitoring boundaries when meta-analyses fall short of a required IS may reduce the risk of false positive results and important inaccurate effect estimates.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primary10.1093/ije/dyn179-
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/18824467-
heal.identifier.secondaryhttp://ije.oxfordjournals.org/content/38/1/276.full.pdf-
heal.journalNameInt J Epidemiolen
heal.journalTypepeer-reviewed-
heal.languageen-
heal.publicationDate2009-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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