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Missing Data and Variable Selection Methods for Cure Models in Cancer Research

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자료유형	E-Book
개인저자	Beesley, Lauren J.
단체저자명	University of Michigan. Biostatistics.
서명/저자사항	Missing Data and Variable Selection Methods for Cure Models in Cancer Research.
발행사항	[S.l.] : University of Michigan., 2018
발행사항	Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항	301 p.
소장본 주기	School code: 0127.
ISBN	9780438127227
일반주기	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. Adviser: Jeremy Michael George Taylor.
요약	In survival analysis, a common assumption is that all subjects will eventually experience the event of interest given long enough follow-up time. However, there are many settings in which this assumption does not hold. For example, suppose we ar
요약	The Cox proportional hazards (CPH) mixture cure model and a generalization, the multistate cure model, can be used to model time-to-event outcomes in the cure setting. In this dissertation, we will address issues of missing data, variable select
요약	In Chapter II, we propose several chained equations methods for imputing missing covariates under the CPH mixture cure model, and we compare the novel approaches with existing chained equations methods for imputing survival data without a cured
요약	In Chapter III, we develop sequential imputation methods for a general class of models with latent and partially latent variables (of which cure models are an example). In particular, we consider the setting where covariate/outcome missingness d
요약	In Chapter IV, we develop an EM algorithm for fitting the multistate cure model. The existing method for fitting this model requires custom software and can be slow to converge. In contrast, the proposed method can be easily implemented using st
요약	In Chapter V, we propose a generalization of the multistate cure model to incorporate subjects with persistent disease. This model has many parameters, and variable selection/shrinkage methods are needed to aid in estimation. We compare the perf
요약	In Chapter VI, we develop Bayesian methods for performing variable selection when we have order restrictions for model parameters. In particular, we consider the setting in which we have interactions with one or more order-restricted variables.
일반주제명	Biostatistics.
언어	영어
기본자료 저록	Dissertation Abstracts International79-12B(E). Dissertation Abstract International
대출바로가기	http://www.riss.kr/pdu/ddodLink.do?id=T15000609