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dc.contributor.authorJin, Chan
dc.date.accessioned2017-05-08T19:15:32Z
dc.date.available2017-05-08T19:15:32Z
dc.date.issued5/8/2017en
dc.identifier.urihttp://hdl.handle.net/10675.2/621419
dc.description.abstract1:n matched case-control studies are commonly used to evaluate the association between the exposure to a risk factor and a disease, where one case is matched to up till n controls. The odds ratio is typically used to quantify such association. Difficulties in estimating the true odds ratio arise, when the exposure status is unknown for at least one individual in a group. In the case where the exposure status is known for all individuals in a group, the true odds ratio is estimated as the ratio of the counts in the discordant cells of the observed two-by-two table. In the case where all data are independent, the odds ratio is estimated using the cross-product ratio from the observed table. Conditional logistic regression estimates are used for incomplete matching data. In this dissertation we suggest a simple method for estimating the odds ratio when the sample consists of a combination of paired and unpaired observations, with 1:n matching. This method uses a weighted average of the odds ratio calculations described above. This dissertation compares the new method to existing methods via simulation.
dc.relation.urlhttp://search.proquest.com/docview/1899924411?accountid=12365en
dc.subjectLogistic Modelsen
dc.subjectOdds Ratioen
dc.subjectBiostatisticsen
dc.subjectEpidemiologyen
dc.titleA New Method For Analyzing 1:N Matched Case Control Studies With Incomplete Dataen
dc.typeDissertationen
dc.contributor.departmentDepartment of Biostatisctics and Epidemiologyen
dc.language.rfc3066en
dc.date.updated2017-05-08T19:15:34Zen
dc.description.advisorLooney, Stephen W.en
dc.description.degreeDoctor of Philosophy with a Major in Biostatisticsen
dc.description.committeeChen, Jie; Nahman, Stan; Waller, Jennifer; Yang, Francesen
refterms.dateFOA2020-02-12T15:15:53Z
html.description.abstract1:n matched case-control studies are commonly used to evaluate the association between the exposure to a risk factor and a disease, where one case is matched to up till n controls. The odds ratio is typically used to quantify such association. Difficulties in estimating the true odds ratio arise, when the exposure status is unknown for at least one individual in a group. In the case where the exposure status is known for all individuals in a group, the true odds ratio is estimated as the ratio of the counts in the discordant cells of the observed two-by-two table. In the case where all data are independent, the odds ratio is estimated using the cross-product ratio from the observed table. Conditional logistic regression estimates are used for incomplete matching data. In this dissertation we suggest a simple method for estimating the odds ratio when the sample consists of a combination of paired and unpaired observations, with 1:n matching. This method uses a weighted average of the odds ratio calculations described above. This dissertation compares the new method to existing methods via simulation.


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