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Finite mixture models in survival data analysis

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TitleInfo
Title
Finite mixture models in survival data analysis
Name (type = personal)
NamePart (type = family)
Li
NamePart (type = given)
Benjamin Yongbin
NamePart (type = date)
1970-
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Benjamin Yongbin Li
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author
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Cabrera
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Javier
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Javier Cabrera
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Advisory Committee
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chair
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Kolassa
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John
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John Kolassa
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Advisory Committee
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internal member
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Kostis
NamePart (type = given)
William
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William Kostis
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Advisory Committee
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internal member
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Emir
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Birol
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Birol Emir
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2019
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2019-10
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English
Abstract (type = abstract)
In the pharmaceutical industry, cost-effectiveness analysis is an important step in the development of new health interventions. It is a method for assessing the gains in health relative to the costs of different health interventions. This assessment helps the regulators, providers, and potential users to make informed decisions. Health gains can be measured in several ways. One of them is the estimated gained life expectancy due to the intervention. Although the randomized controlled trials (RCTs) are considered to be the most reliable sources of the evidence to be used in the cost-effectiveness analysis, data collected from these trials are often incomplete due to censoring and truncation. This requires the extrapolation of the survival probability beyond the time frame of the RCTs. For this purpose, parametric models are necessary to estimate the survival functions. Although there exist several single parametric models (such as the Weibull, Gamma, and lognormal) that can perform this task, they fail to provide accurate estimates when the survival data are heterogeneous. In these situations, the finite mixture models fit the data better and therefore their results are more consistent and reliable.
This dissertation studies the implementation of the finite mixture models in survival data analysis. It discusses in detail how to estimate the parameters of a finite mixture models through the expectation and maximization (EM) algorithm. These steps are flexible to account for the effects of covariates. In addition, we propose a new approach via censored quantile regression for finding the initial values of the EM algorithm. This method takes into consideration the special features of survival data and therefore will help improve the efficiency of the EM algorithm. We also demonstrate how to construct the desired confidence intervals of the estimates through bootstrapping.
In oncology as well as other therapeutic areas, some patients will not experience the relapse of the disease after being treated. These patients are considered to be cured. It is of interest to know both the cure rate and the survival function of the patients who are not cured by the intervention. We study the mixture cure model in the general framework of finite mixture models as a special case, and provide the modified EM algorithm to estimate both the cure rate and the survival function of the uncured patients.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
Subject (authority = local)
Topic
Finite mixture model
Subject (authority = LCSH)
Topic
Patients -- Mortality -- Statistical methods
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_10297
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application/pdf
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text/xml
Extent
1 online resource (xi, 97 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-sf07-7e70
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Li
GivenName
Benjamin
Role
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RightsEvent
Type
Permission or license
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2019-09-23 08:39:25
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Name
Benjamin Li
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2019-09-23T14:33:02
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