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dc.contributor.advisorMaWhinney, Samantha
dc.contributor.authorMoore, Camille Marie
dc.contributor.committeememberCarlson, Nichole
dc.contributor.committeememberForster, Jeri
dc.date.accessioned2007-01-03T04:53:19Z
dc.date.available2014-06-30T08:20:26Z
dc.date.submitted2013
dc.descriptionIncludes bibliographical references.
dc.description111 p.
dc.description.abstractDropout is a common source of missing data in longitudinal studies, and often occurs for reasons that may be related to an outcome of interest. When dropout depends on unobserved outcomes, even after conditioning on observable data, data are potentially missing not at random and dropout is therefore not ignorable. When the dropout mechanism is unspecified, semiparametric varying coefficient models can be used to account for non-ignorable dropout. This method is more robust than the parametric conditional linear approach. However, fitting these varying coefficient models requires the specification of the number and location of spline knots, which may influence model fit. We present simulation results comparing the natural cubic B spline varying coefficient method with a knot location selection algorithm to the same method with knots evenly placed at the quantiles of the dropout distribution, as well as to the classic conditional linear model. In addition, semiparametric varying coefficient models accounting for dropout time are extended to account for dropout reason as well. These methods are applied to data from the Acute Infection and Early Disease Research Program (AIEDRP) to determine the effect of injection drug use on the longitudinal trajectory of CD4+ T cell count in untreated HIV seropositive subjects.
dc.identifierMoore_ucdenveramc_1639M_10042.pdf
dc.identifier.urihttp://hdl.handle.net/10968/254
dc.languageEnglish
dc.publisherUniversity of Colorado Anschutz Medical Campus. Strauss Health Sciences Library
dc.rightsCopyright of the original work is retained by the author.
dc.rights.access1-year embargo
dc.rights.accessAccess restricted until June 30, 2014.
dc.subjectmissing data
dc.subjectvarying coefficient models
dc.subjectsplines
dc.subject.meshStatistics, Nonparametric
dc.subject.meshHIV
dc.subject.meshBiostatistics
dc.subject.meshPatient Dropouts
dc.titleKnot selection strategies for semiparametric varying coefficient models applied to longitudinal cohorts with multiple dropout reasons
dc.typeText
dcterms.embargo.expires2014-06-30
thesis.degree.disciplineBiostatistics
thesis.degree.grantorUniversity of Colorado at Denver, Anschutz Medical Campus
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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