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Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data

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개인저자Dharmarajan, Sai Hurrish.
단체저자명University of Michigan. Biostatistics.
서명/저자사항Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data.
발행사항[S.l.] : University of Michigan., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항127 p.
소장본 주기School code: 0127.
ISBN9780438125452
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Douglas E. Schaubel.
요약In this dissertation, we propose new methods for analysis of clustered competing risks data (Chapters 1 and 2) and for instrumental variable (IV) analysis of univariate censored time-to-event data and competing risks data (Chapters 3 and 4).
요약In Chapter 1, we propose estimating center effects through cause-specific proportional hazards frailty models that allow correlation among a center's cause-specific effects. To evaluate center performance, we propose a directly standardized exce
요약In Chapter 2, we propose to model the effects of cluster and individual-level covariates directly on the cumulative incidence functions of each risk through a semiparametric mixture component model with cluster-specific random effects. Our model
요약In Chapter 3, we turn our focus to causal inference in the censored time-to-event setting in the presence of unmeasured confounders. We develop weighted IV estimators of the complier average causal effect on the restricted mean survival time. Ou
요약In Chapter 4, we develop IV analysis methods for competing risks data. Our method permits simultaneous inference of exposure effects on the absolute risk of all competing events and accommodates exposure dependent censoring. We apply the methods
일반주제명Biostatistics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000448

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