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AuthorStone, Jennifer C
AuthorGlass, Kathryn
AuthorMunn, Zachary
AuthorTugwell, Peter
AuthorDoi, Suhail A R
Available date2019-12-15T04:55:16Z
Publication Date2019-09-01
Publication NameJournal of Clinical Epidemiology
Identifierhttp://dx.doi.org/10.1016/j.jclinepi.2019.09.010
CitationJ.C. Stone et al. "Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches" Journal of Clinical Epidemiology 117 (2020) 36e45
ISSN0895-4356
URIhttp://hdl.handle.net/10576/12396
AbstractThe 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. 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. Use of the two examples from the literature suggest that all methods provide similar adjustment. Methods differed mainly in applicability and limitations. 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.
Languageen
PublisherElsevier
SubjectBias adjustment
Meta-analysis
Quality assessment
Quality score
Risk of bias
Stratification
TitleComparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches.
TypeArticle
Pagination36-45
Volume Number117
ESSN1878-5921
dc.accessType Abstract Only


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