Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches

dc.contributor.authorStone, Jennifer
dc.contributor.authorGlass, Kathryn
dc.contributor.authorMunn, Zachary
dc.contributor.authorTugwell, Peter
dc.contributor.authorDoi, Suhail A R
dc.date.accessioned2020-06-29T23:58:58Z
dc.date.issued2020
dc.date.updated2020-01-27T16:08:45Z
dc.description.abstractBackground The quality of primary research is commonly assessed before inclusion in meta-analyses. Findings are discussed in the context of the quality appraisal by categorizing studies according to risk of bias. The impact of appraised risk of bias on study outcomes is typically judged by the reader; however, several methods have been developed to quantify this risk of bias assessment and incorporate it into the pooled results of meta-analysis, a process known as bias adjustment. The advantages, potential limitations, and applicability of these methods are not well defined. Study Design and Setting Comparative evaluation of the applicability of the various methods and their limitations are discussed using two examples from the literature. These methods include weighting, stratification, regression, use of empirically based prior distributions, and elicitation by experts. Results Use of the two examples from the literature suggest that all methods provide similar adjustment. Methods differed mainly in applicability and limitations. Conclusion Bias adjustment is a feasible process in meta-analysis with several strategies currently available. Quality effects modelling was found to be easily implementable with fewer limitations in comparison to other methods.
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0895-4356en_AU
dc.identifier.urihttp://hdl.handle.net/1885/205636
dc.language.isoen_AUen_AU
dc.publisherElsevier Ltden_AU
dc.rights© 2019 Elsevier Inc.en_AU
dc.sourceJournal of Clinical Epidemiologyen_AU
dc.titleComparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approachesen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.lastpage45en_AU
local.bibliographicCitation.startpage36en_AU
local.contributor.affiliationStone, Jennifer, College of Health and Medicine, ANUen_AU
local.contributor.affiliationGlass, Kathryn, College of Health and Medicine, ANUen_AU
local.contributor.affiliationMunn, Zachary, The University of Adelaideen_AU
local.contributor.affiliationTugwell, Peter, University of Ottawaen_AU
local.contributor.affiliationDoi, Suhail A R, Qatar Universityen_AU
local.contributor.authoremailu4053649@anu.edu.auen_AU
local.contributor.authoruidStone, Jennifer, u6318430en_AU
local.contributor.authoruidGlass, Kathryn, u4053649en_AU
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor111706 - Epidemiologyen_AU
local.identifier.absseo920204 - Evaluation of Health Outcomesen_AU
local.identifier.ariespublicationU1070655xPUB148en_AU
local.identifier.citationvolume117en_AU
local.identifier.doi10.1016/j.jclinepi.2019.09.010en_AU
local.identifier.uidSubmittedByU1070655en_AU
local.publisher.urlhttps://www.elsevier.com/en-auen_AU
local.type.statusPublished Versionen_AU

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