Comparing Predictive Values of Two diagnostic tests

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dc.contributor.advisor Anastasios A. Tsiatis, Committee Co-Chair en_US
dc.contributor.advisor Jason Osborne, Committee Member en_US
dc.contributor.advisor Daowen Zang, Committee Member en_US
dc.contributor.advisor Andrzej Kosinski , Committee Chair en_US
dc.contributor.author Cho, Yoonjin en_US
dc.date.accessioned 2010-04-02T19:11:21Z
dc.date.available 2010-04-02T19:11:21Z
dc.date.issued 2009-07-29 en_US
dc.identifier.other etd-12052008-101443 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/5305
dc.description.abstract Positive and negative predictive values are important measures of accuracy when one compares the accuracy of diagnostic tests. When more than one diagnostic tests are available, one may has to choose one of the possible diagnostic tests due to cost, time, or ethical reason. We consider a pair study design on cohort study where two diagnostic tests are measured on every patients. Our parameter of interest is the log odds of predictive values. In first chapter, we review current methods on comparing diagnostic tests when gold standards are available on every individual. We propose our method by series of logistic regressions and derive estimator and test statistics based on likelihood method. However, it is often the case that gold standard is not observed on every patient because it may be invasive. If we only consider those who have observed gold standard, the estimator may not be biased. In Chapter 2 and 3, we extend the cases when gold standard is missing. We assume that missing gold standard is missing at random, which is to depend on observed data. In Chapter 2, we use semiparametric theory to derive a class of regular and asymptotically normal of our parameter of interest. Out of the class, we derive an estimator which is the most effcient in the class in using the information from available auxiliary covariates which may be associated with the outcome of gold standard. We also use auxiliary covariates in modeling the probability of observing gold standard. In Chapter 3, through M-estimator, we derive another consistent estimator through imputation method. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Diagnostic Tests en_US
dc.subject Semiparametric Theory en_US
dc.subject Missing Data en_US
dc.subject M-estimator en_US
dc.title Comparing Predictive Values of Two diagnostic tests en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Statistics en_US


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