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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/24230
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DC FieldValueLanguage
dc.contributor.advisorWalter, Stephen D.-
dc.contributor.authorTorrance, Virginia L.-
dc.date.accessioned2019-04-06T18:52:06Z-
dc.date.available2019-04-06T18:52:06Z-
dc.date.issued1994-
dc.identifier.urihttp://hdl.handle.net/11375/24230-
dc.description.abstractLatent class modelling is one method used in the evaluation of diagnostic tests when there is no gold standard test that is perfectly accurate. The technique demonstrates maximum likelihood estimates of the prevalence of a disease or a condition and the error rates of diagnostic tests or observers. This study reports the effect of departures from the latent class model assumption of independent misclassifications between observers or tests conditional on the true state of the individual being tested. It is found that estimates become biased in the presence of dependence. Most commonly the prevalence of the disease is overestimated when the true prevalence is at less than 50% and the error rates of dependent observers are underestimated. If there are also independent observers in the group, their error rates are overestimated. The most dangerous scenario in which to use latent class methods int he evaluation of tests is when the true prevalence is low and the false positive rate is high. This is common to many screening situations.en_US
dc.language.isoenen_US
dc.subjectdiagnostic, tests, misclassification errors, dependent, latent class analysisen_US
dc.titleLatent Class Analysis of Diagnostic Tests: The Effect of Dependent Misclassification Errorsen_US
dc.title.alternativeLatent Class Analysis: Dependent Misclassification Errorsen_US
dc.typeThesisen_US
dc.contributor.departmentMathematics and Statisticsen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Science (MS)en_US
Appears in Collections:Digitized Open Access Dissertations and Theses

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