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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Multivariate dependencies in survival analysis / Amy Beatrix Salter.

Salter, Amy Beatrix January 1999 (has links)
Bibliography: leaves 177-181. / xiv, 181 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis investigates determinants of factors associated with retention of injecting drug users on the South Australian methadone program over the decade 1981 to mid 1991. Truncated multivariate survival models are proposed for the analysis of data from the program, and the theory of graphical chain models applied to the data. A detailed analysis is presented which gives further insight into the nature of the relationships that exist amongst these data. This provides an application of graphical chain models to survival data. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 2000
2

Multivariate dependencies in survival analysis /

Salter, Amy Beatrix. January 1999 (has links) (PDF)
Thesis (Ph.D.) -- University of Adelaide, Dept. of App. Maths, 2000. / Bibliography: leaves 177-181.
3

Multivariate dependencies in survival analysis

Salter, Amy Beatrix. January 1999 (has links) (PDF)
Bibliography: leaves 177-181. This thesis investigates determinants of factors associated with retention of injecting drug users on the South Australian methadone program over the decade 1981 to mid 1991. Truncated multivariate survival models are proposed for the analysis of data from the program, and the theory of graphical chain models applied to the data. A detailed analysis is presented which gives further insight into the nature of the relationships that exist amongst these data. This provides an application of graphical chain models to survival data.
4

A multivariate frailty model for disease recurrences and survival.

Wen, Sijin. Chan, Wenyaw, Xiong, Momiao, January 2009 (has links)
Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1744. Advisers: Xuelin Huang; Ralph F. Frankowski. Includes bibliographical references.
5

Multilevel models for survival analysis in dental research

Wong, Chun-mei, May. January 2005 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.
6

A new framework for nonparametric estimation of the bivariate survivor function /

Moodie, Felicity Zoe. January 2001 (has links)
Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 131-134).
7

Analysis of clustered grouped survival data /

Ip, Ying-Kit, David. January 2001 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 91-97).
8

Bayesian survival analysis

Abrams, Keith Rowland January 1992 (has links)
In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model allows for the fact that patients in the trial will have been observed for different lengths of time and for some patients the time to the event of interest will not be observed (censored). The regression structure means that a measure of treatment effect can be obtained after allowing for other important factors. Clinical trials are not conducted in isolation, but are part of an on-going learning process. In order to assess the current weight of evidence for the use of a particular treatment a Bayesian approach is necessary. Such an approach allows for the formal inclusion of prior information, either in the form of clinical expertise or the results from previous studies, into the statistical analysis. An initial Bayesian analysis, for a single non-recurrent event, can be performed using non-temporal models that consider the occurrence of events up to a specific time from entry into the study. Although these models are conceptually simple, they do not explicitly allow for censoring or covariates. In order to address both of these deficiencies a Bayesian fully parametric multiplicative intensity regression model is developed. The extra complexity of this model means that approximate integration techniques are required. Asymptotic Laplace approximations and the more computer intensive Gauss-Hermite quadrature are shown to perform well and yield virtually identical results. By adopting counting process notation the multiplicative intensity model is extended to the multi-state scenario quite easily. These models are used in the analysis of a cancer clinical trial to assess the efficacy of neutron therapy compared to standard photon therapy for patients with cancer of the pelvic region. In this trial there is prior information both in the form of clinical prior beliefs and results from previous studies. The usefulness of multi-state models is also demonstrated in the analysis of a pilot quality of life study. Bayesian multi-state models are shown to provide a coherent framework for the analysis of clinical studies, both interventionist and observational, yielding clinically meaningful summaries about the current state of knowledge concerning the disease/treatment process.
9

Analysis of clustered grouped survival data

葉英傑, Ip, Ying-Kit, David. January 2001 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
10

Direct adjustment method on Aalen's additive hazards model for competing risks data

Akcin, Haci Mustafa. January 2008 (has links)
Thesis (M.S.)--Georgia State University, 2008. / Title from file title page. Xu Zhang, committee chair; Yichuan Zhao, Jiawei Liu, Yu-Sheng Hsu, committee members. Electronic text (51 p.) : digital, PDF file. Description based on contents viewed July 15, 2008. Includes bibliographical references (p. 50-51).

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