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dc.contributor.authorPowell, Adeya Shontelle
dc.date.accessioned2014-03-04T20:59:49Z
dc.date.available2014-03-04T20:59:49Z
dc.date.issued2012-12
dc.identifier.otherpowell_adeya_s_201212_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/powell_adeya_s_201212_ms
dc.identifier.urihttp://hdl.handle.net/10724/28582
dc.description.abstractMeasurement error is inherent in the collection of tumor markers. In general, when measurement error is present we know that the regression parameters are bias, but for a logistic model there are other concerns. This research sought to answer what happens to Specificity, area under the curve (AUC), Sensitivity, and the classification accuracy when measurement error was present. We found that there was better discrimination for tumor markers highly correlated with the dichotomous outcome variable; and Specificity, or true negatives, decreased as measurement error increased indicating an increase in the number of false negatives in the presence of measurement error.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectMeasurement error, Tumor Markers, AUC, Specificity, Sensitivity, Logistic Regression, Classification, Accuracy, Bias
dc.titleThe influence of measurement errors in tumor markers
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorWilliam P. McCormick
dc.description.advisorKevin Dobbins
dc.description.committeeWilliam P. McCormick
dc.description.committeeKevin Dobbins
dc.description.committeeT. N. Sriram
dc.description.committeeCheolwoo Park


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