Measurement error in linear mixed models

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dc.contributor.advisorKim, Hyang Mi
dc.contributor.authorLawal, Oluwaseyi Adetutu
dc.date.accessioned2017-12-18T22:36:43Z
dc.date.available2017-12-18T22:36:43Z
dc.date.issued2012
dc.descriptionBibliography: p. 46-50en
dc.description.abstractMeasurement error of exposure is widely acknowledged as pervasive and often important source of bias or misleading results in much research. There has been an abundance of interest in the topic of covariate measurement error and there is an extensive literature in measurement error models and methods. Clustered data can be defined as data in which the observations are grouped into disjoint classes, called clusters, according to some classification criterion. Mixed models were developed to handle clustered data and have received a great deal of attention in the statistical literature for the past years because of the flexibility they offer in handling the unbalanced clustered data that arise in many areas of investigation. In this thesis we consider both linear and linear mixed effect models with measureĀ­ment error. Three methods are compared through simulation studies, namely the naive method, the two-step approach, and the likelihood estimation method. The naive method ignores covariate measurement error in models, the regression calibration method, as a two-step approach, is a commonly used simple approach and may be applicable to alĀ­most any regression models, and the Expectation Maximization (EM) algorithm, as a likelihood method, treats random effects as missing data. Naive approaches are shown to be inadequate to be used when covariates are subject to error. Both the regression calibration method and the EM method appear to be good, but the regression calibration method is much simpler than the EM method. We illustrate these methods in HIV study data.
dc.format.extentvii, 50 leaves : ill. ; 30 cm.en
dc.identifier.citationLawal, O. A. (2012). Measurement error in linear mixed models (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/5014en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/5014
dc.identifier.urihttp://hdl.handle.net/1880/106015
dc.language.isoeng
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleMeasurement error in linear mixed models
dc.typemaster thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 2111 627942981
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
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