A New Method For Analyzing 1:N Matched Case Control Studies With Incomplete Data

Hdl Handle:
http://hdl.handle.net/10675.2/621419
Title:
A New Method For Analyzing 1:N Matched Case Control Studies With Incomplete Data
Authors:
Jin, Chan
Abstract:
1: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.
Issue Date:
8-May-2017
URI:
http://hdl.handle.net/10675.2/621419
Appears in Collections:
Department of Biostatistics and Epidemiology Theses and Dissertations; Theses and Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.authorJin, Chanen
dc.date.accessioned2017-05-08T19:15:32Z-
dc.date.available2017-05-08T19:15:32Z-
dc.date.issued2017-05-08-
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.en
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.language.rfc3066en-
dc.date.updated2017-05-08T19:15:34Zen
dc.description.advisorLooney, Stephen W.en
dc.description.committeeChen, Jie; Nahman, Stan; Waller, Jennifer; Yang, Francesen
dc.description.degreeDoctor of Philosophy with a Major in Biostatisticsen
dc.identifier.urlhttp://ezproxy.gru.edu/login?url=http://search.proquest.com/docview/1899924411?accountid=12365en
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