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dc.contributor.advisorHill, Jeanne
dc.contributor.authorHainline, Allison
dc.date.accessioned2013-05-24T20:15:44Z
dc.date.available2013-05-24T20:15:44Z
dc.date.copyright2013
dc.date.issued2013-05-24
dc.identifier.urihttp://hdl.handle.net/2104/8680
dc.description.abstractBiostatistical studies of medical data are extremely important in distinguishing relationships between drugs or treatments and the patient's medical response. These studies generally use data from large health care databases, which provide immense amounts of information while allowing the researcher to analyze long-term effects that may not be shown in a typical randomized controlled trial. However, when using large databases, one must be particularly aware of the effect of unmeasured confounding on statistical models. Confounding arises when factors unrelated to the particular study have a hidden effect on observed health outcomes. Bayesian statistics provides a mechanism for model fitting which synthesizes the data with prior information about bias, allowing the researcher to control confounding through the inclusion of additional variables from independent datasets. In this thesis I will provide a background of the proposed method as well its application to two independent analyses: predictors of low birth weight babies and predictors of parental separation anxiety.en_US
dc.language.isoen_USen_US
dc.rightsBaylor University projects are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.en_US
dc.subjectStatistics.en_US
dc.subjectBayesian Statistics.en_US
dc.subjectConfounding.en_US
dc.titleFrequentist and Bayesian Modeling in the Presence of Unmeasured Confoundingen_US
dc.typeThesisen_US
dc.rights.accessrightsWorldwide accessen_US
dc.contributor.departmentStatistics.en_US
dc.contributor.schoolsHonors College.en_US


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