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

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개인저자Beesley, Lauren J.
단체저자명University of Michigan. Biostatistics.
서명/저자사항Missing Data and Variable Selection Methods for Cure Models in Cancer Research.
발행사항[S.l.] : University of Michigan., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항301 p.
소장본 주기School code: 0127.
ISBN9780438127227
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Jeremy Michael George Taylor.
요약In 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
요약The 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
요약In 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
요약In 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
요약In 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
요약In 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
요약In 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.
일반주제명Biostatistics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000609

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