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Analysis of failure time data under risk set sampling and missing covariates /Qi, Lihong. January 2003 (has links)
Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (p. 141-146).
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Instrumental variables in survival analysis /Harvey, Danielle J. January 2001 (has links)
Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, August 2001. / Includes bibliographical references. Also available on the Internet.
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The use of weighted logrank statistics in group sequential testing and non-proportional hazards /Gillen, Daniel L., January 2003 (has links)
Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (p. 158-160).
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Analysis of interval-censored failure time data with long-term survivorsWong, Kin-yau., 黃堅祐. January 2012 (has links)
Failure time data analysis, or survival analysis, is involved in various research
fields, such as medicine and public health. One basic assumption in
standard survival analysis is that every individual in the study population
will eventually experience the event of interest. However, this assumption is
usually violated in practice, for example when the variable of interest is the
time to relapse of a curable disease resulting in the existence of long-term survivors.
Also, presence of unobservable risk factors in the group of susceptible
individuals may introduce heterogeneity to the population, which is not properly
addressed in standard survival models. Moreover, the individuals in the
population may be grouped in clusters, where there are associations among observations
from a cluster. There are methodologies in the literature to address
each of these problems, but there is yet no natural and satisfactory way to
accommodate the coexistence of a non-susceptible group and the heterogeneity
in the susceptible group under a univariate setting. Also, various kinds of
associations among survival data with a cure are not properly accommodated.
To address the above-mentioned problems, a class of models is introduced to
model univariate and multivariate data with long-term survivors.
A semiparametric cure model for univariate failure time data with long-term
survivors is introduced. It accommodates a proportion of non-susceptible
individuals and the heterogeneity in the susceptible group using a compound-
Poisson distributed random effect term, which is commonly called a frailty. It
is a frailty-Cox model which does not place any parametric assumption on the
baseline hazard function. An estimation method using multiple imputation
is proposed for right-censored data, and the method is naturally extended to
accommodate interval-censored data. The univariate cure model is extended
to a multivariate setting by introducing correlations among the compound-
Poisson frailties for individuals from the same cluster. This multivariate cure
model is similar to a shared frailty model where the degree of association among
each pair of observations in a cluster is the same. The model is further extended
to accommodate repeated measurements from a single individual leading to
serially correlated observations. Similar estimation methods using multiple
imputation are developed for the multivariate models. The univariate model
is applied to a breast cancer data and the multivariate models are applied
to the hypobaric decompression sickness data from National Aeronautics and
Space Administration, although the methodologies are applicable to a wide
range of data sets. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
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Estimation for generalized linear mixed model via multipleimputationsTang, On-yee., 鄧安怡. January 2005 (has links)
published_or_final_version / abstract / toc / Statistics and Actuarial Science / Master / Master of Philosophy
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Estimation of survival of left truncated and right censored data under increasing hazardShinohara, Russell. January 2007 (has links)
When subjects are recruited through a cross-sectional survey they have already experienced the initiation of the event of interest, say the onset of a disease. This method of recruitment results in the fact that subjects with longer duration of the disease have a higher chance of being selected. It follows that censoring in such a case is not non-informative. The application of standard techniques for right-censored data thus introduces a bias to the analysis; this is referred to as length-bias. This paper examines the case where the subjects are assumed to enter the study at a uniform rate, allowing for the analysis in a more efficient unconditional manner. In particular, a new method for unconditional analysis is developed based on the framework of a conditional estimator. This new method is then applied to the several data sets and compared with the conditional technique of Tsai [23].
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Inference for Cox's regression model via a new version of empirical likelihoodJinnah, Ali. January 2007 (has links)
Thesis (M.S.)--Georgia State University, 2007. / Title from file title page. Yichuan Zhao, committee chair; Yu-Sheng Hsu , Xu Zhang, Yuanhui Xiao , committee members. Electronic text (54 p.) : digital, PDF file. Description based on contents viewed Feb. 25, 2008. Includes bibliographical references (p. 30-32).
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Parametric potential-outcome survival models for causal inference : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy, University of Canterbury /Gong, Zhaojing. January 1900 (has links)
Thesis (Ph. D.)--University of Canterbury, 2008. / Typescript (photocopy). "October 2008." Includes bibliographical references (p. 230-243). Also available via the World Wide Web.
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Statistical inference for banding dataLiu, Fei, January 2008 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 94-97) Also available in print.
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Bayesian model averaging for censored survival models /Volinsky, Christopher T., January 1997 (has links)
Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (p. [132]-146).
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