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dc.contributor.authorOrtega Villa, Ana Mariaen
dc.date.accessioned2015-11-24T15:22:25Zen
dc.date.available2015-11-24T15:22:25Zen
dc.date.issued2015-11-23en
dc.identifier.othervt_gsexam:6564en
dc.identifier.urihttp://hdl.handle.net/10919/64181en
dc.description.abstractSemiparametric modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown form. In this dissertation we made contributions to semiparametric modeling for matched case-crossover data. In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. Any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. However, some matching covariates such as time, and/or spatial location often play an important role as an effect modification. Failure to include them makes incorrect statistical estimation, prediction and inference. Hence in this dissertation, we propose several approaches that will allow the inclusion of time and spatial location as well as other effect modifications such as heterogeneous subpopulations among the data. To address modification due to time, three methods are developed: the first is a parametric approach, the second is a semiparametric penalized approach and the third is a semiparametric Bayesian approach. We demonstrate the advantage of the one stage semiparametric approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. To address modifications due to time and spatial location, two methods are developed: the first one is a semiparametric spatial-temporal varying coefficient model for a small number of locations. The second method is a semiparametric spatial-temporal varying coefficient model, and is appropriate when the number of locations among the subjects is medium to large. We demonstrate the accuracy of these approaches by using simulation studies, and when appropriate, an epidemiological example of a 1-4 bi-directional case-crossover study. Finally, to explore further effect modifications by heterogeneous subpopulations among strata we propose a nonparametric Bayesian approach constructed with Dirichlet process priors, which clusters subpopulations and assesses heterogeneity. We demonstrate the accuracy of our approach using a simulation study, as well a an example of a 1-4 bi-directional case-crossover study.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBayesian Nonparametricen
dc.subjectConditional logistic regressionen
dc.subjectMatched case-control studyen
dc.subjectRegression splinesen
dc.subjectSpatial-temporal dataen
dc.subjectVarying Coefficient Modelen
dc.titleSemiparametric Varying Coefficient Models for Matched Case-Crossover Studiesen
dc.typeDissertationen
dc.contributor.departmentStatisticsen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineStatisticsen
dc.contributor.committeechairKim, Inyoungen
dc.contributor.committeememberGuo, Fengen
dc.contributor.committeememberTerrell, George R.en
dc.contributor.committeememberFranck, Christopher T.en


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