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The divide-and-combine approaches for multivariate survival analysis and multistate survival analysis in big data

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Title
The divide-and-combine approaches for multivariate survival analysis and multistate survival analysis in big data
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Wei
NamePart (type = date)
1988-
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Wei Wang
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Lu
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Shou-En
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Shou-En Lu
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Advisory Committee
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chair
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LIN
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YONG
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YONG LIN
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Yaqun
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Yaqun Wang
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Advisory Committee
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RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Kim
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Sinae
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Sinae Kim
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Advisory Committee
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RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Xie
NamePart (type = given)
Minge
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Minge Xie
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Advisory Committee
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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NamePart
School of Graduate Studies
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school
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theses
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ETD doctoral
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2020
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2020-10
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2020
Language
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English
Abstract (type = abstract)
Multivariate failure time data can be unordered or ordered, which can be analyzed using multivariate survival analysis and multistate survival analysis, respectively. When sample sizes are extraordinarily large, both analyses could face computational challenges. In this dissertation, we propose divide-and-combine approaches to analyze large-scale multivariate failure time data in both multivariate survival analysis and multistate survival analysis. Our approaches are motivated by the Myocardial Infarction Data Acquisition System (MIDAS), a New Jersey statewide database that includes 73,725,160 admissions to non-federal hospitals and emergency rooms (ERs) from 1995 to 2017. We propose to randomly divide the full data into multiple subsets and propose a weighted method to combine these estimators obtained from individual subsets. In divided subsets, estimated regression parameters and estimated cumulative hazards are calculated, respectively, for multivariate survival analysis and multistate survival analysis. Under mild conditions, we show that the combined estimators are asymptotically equivalent to the estimators obtained from the full data as if the data were analyzed all at once. In addition, to screen out risk factors with weak signals in multivariate survival analysis, we propose to perform the regularized estimation on the combined estimators using their combined confidence distributions. Theoretical properties of proposed approaches, such as asymptotic equivalence between divide-and-combine analysis and full-data analysis, estimation consistency, selection consistency, and oracle properties are studied. Performances of proposed estimators are investigated using simulation studies. The MIDAS data are used to illustrate our proposed methodologies.
Subject (authority = local)
Topic
Confidence distribution
Subject (authority = RUETD)
Topic
Public Health
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_11167
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1 online resource (xi, 144 pages)
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-s5sq-vq68
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Wang
GivenName
Wei
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-09-20 22:50:00
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Name
Wei Wang
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Type
License
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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.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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