Show simple item record

dc.contributor.authorGumedze, Freedom N.
dc.contributor.authorJackson, Dan
dc.date.accessioned2011-06-16T15:46:03Z
dc.date.available2011-06-16T15:46:03Z
dc.date.issued2011-02-16
dc.identifier.citationBMC Medical Research Methodology 2011, 11:19
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/237761
dc.descriptionRIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.
dc.description.abstractAbstract Background Meta-analysis typically involves combining the estimates from independent studies in order to estimate a parameter of interest across a population of studies. However, outliers often occur even under the random effects model. The presence of such outliers could substantially alter the conclusions in a meta-analysis. This paper proposes a methodology for identifying and, if desired, downweighting studies that do not appear representative of the population they are thought to represent under the random effects model. Methods An outlier is taken as an observation (study result) with an inflated random effect variance. We used the likelihood ratio test statistic as an objective measure for determining whether observations have inflated variance and are therefore considered outliers. A parametric bootstrap procedure was used to obtain the sampling distribution of the likelihood ratio test statistics and to account for multiple testing. Our methods were applied to three illustrative and contrasting meta-analytic data sets. Results For the three meta-analytic data sets our methods gave robust inferences when the identified outliers were downweighted. Conclusions The proposed methodology provides a means to identify and, if desired, downweight outliers in meta-analysis. It does not eliminate them from the analysis however and we consider the proposed approach preferable to simply removing any or all apparently outlying results. We do not however propose that our methods in any way replace or diminish the standard random effects methodology that has proved so useful, rather they are helpful when used in conjunction with the random effects model.
dc.language.isoen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.titleA random effects variance shift model for detecting and accommodating outliers in meta-analysis
dc.typeArticle
dc.type.versionPublished Version
dc.date.updated2011-06-16T15:46:03Z
dc.rights.holderGumedze et al.; licensee BioMed Central Ltd.
pubs.declined2017-10-11T13:54:29.394+0100
rioxxterms.versionofrecord10.1186/1471-2288-11-19


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record