Thesis (Ph.D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 6, 2009) Includes bibliographical references.

published_or_final_version / abstract / Statistics / Doctoral / Doctor of Philosophy

No description available.

Thesis (Ph.D.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 151-153.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2001. / Bibliography: leaves 87-89.

Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, August 2001. / Includes bibliographical references. Also available on the Internet.

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

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].

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 94-95). Also available via the Internet.

Thesis (Ph. D.)--University of Hong Kong, 1995. / "June 1994." Includes bibliographical references (leave 149-161).