Model-Assisted Estimators for Time-to-Event Data

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dc.contributor.advisorValliant, Richarden_US
dc.contributor.authorReist, Benjamin Martinen_US
dc.contributor.departmentSurvey Methodologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-01-23T06:35:09Z
dc.date.available2018-01-23T06:35:09Z
dc.date.issued2017en_US
dc.description.abstractIn this dissertation, I develop model-assisted estimators for estimating the proportion of a population that experienced some event by time t. I provide the theoretical justification for the new estimators using time-to-event models as the underlying framework. Using simulation, I compared these estimators to traditional methods, then I applied the estimators to a study of nurses’ health, where I estimated the proportion of the population that had died after a certain period of time. The new estimators performed as well if not better than existing methods. Finally, as this work assumes that all units are censored at the same point in time, I propose an extension that allows units censoring time to vary.en_US
dc.identifierhttps://doi.org/10.13016/M2X34MT4Q
dc.identifier.urihttp://hdl.handle.net/1903/20303
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledBiostatisticsen_US
dc.subject.pquncontrolledComplex Surveysen_US
dc.subject.pquncontrolledGeneralized Difference Estimatorsen_US
dc.subject.pquncontrolledModel Calibration Estimatorsen_US
dc.subject.pquncontrolledTime to Event Modelsen_US
dc.titleModel-Assisted Estimators for Time-to-Event Dataen_US
dc.typeDissertationen_US

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