A random effects variance shift model for detecting and accommodating outliers in meta-analysis

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dc.contributor.authorGumedze, Freedomen_ZA
dc.contributor.authorJackson, Danen_ZA
dc.date.accessioned2015-10-28T07:04:15Z
dc.date.available2015-10-28T07:04:15Z
dc.date.issued2011en_ZA
dc.description.abstractBACKGROUND: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.en_ZA
dc.identifier.apacitationGumedze, F., & Jackson, D. (2011). A random effects variance shift model for detecting and accommodating outliers in meta-analysis. <i>BMC Medical Research Methodology</i>, http://hdl.handle.net/11427/14468en_ZA
dc.identifier.chicagocitationGumedze, Freedom, and Dan Jackson "A random effects variance shift model for detecting and accommodating outliers in meta-analysis." <i>BMC Medical Research Methodology</i> (2011) http://hdl.handle.net/11427/14468en_ZA
dc.identifier.citationGumedze, F. N., & Jackson, D. (2011). A random effects variance shift model for detecting and accommodating outliers in meta-analysis. BMC medical research methodology, 11(1), 19.en_ZA
dc.identifier.ris TY - Journal Article AU - Gumedze, Freedom AU - Jackson, Dan AB - 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. DA - 2011 DB - OpenUCT DO - 10.1186/1471-2288-11-19 DP - University of Cape Town J1 - BMC Medical Research Methodology LK - https://open.uct.ac.za PB - University of Cape Town PY - 2011 T1 - A random effects variance shift model for detecting and accommodating outliers in meta-analysis TI - A random effects variance shift model for detecting and accommodating outliers in meta-analysis UR - http://hdl.handle.net/11427/14468 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/14468
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2288-11-19
dc.identifier.vancouvercitationGumedze F, Jackson D. A random effects variance shift model for detecting and accommodating outliers in meta-analysis. BMC Medical Research Methodology. 2011; http://hdl.handle.net/11427/14468.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licenseen_ZA
dc.rights.holder2011 Gumedze and Jackson; licensee BioMed Central Ltd.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_ZA
dc.sourceBMC Medical Research Methodologyen_ZA
dc.source.urihttp://www.biomedcentral.com/bmcmedresmethodol/en_ZA
dc.subject.otherAnalysis of Varianceen_ZA
dc.subject.otherData Interpretation, Statisticalen_ZA
dc.subject.otherDental Cariesen_ZA
dc.titleA random effects variance shift model for detecting and accommodating outliers in meta-analysisen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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