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Modeling longitudinal data with mixed dropout mechanisms using extended pattern mixture model
Descriptive
TitleInfo
Title
Modeling longitudinal data with mixed dropout mechanisms using extended pattern mixture model
Name
(type = personal)
NamePart
(type = family)
Gong
NamePart
(type = given)
Jing
NamePart
(type = date)
1974-
DisplayForm
Jing Gong
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Ohman-Strickland
NamePart
(type = given)
Pamela
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Pamela Ohman-Strickland
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Moore
NamePart
(type = given)
Dirk
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Dirk Moore
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Kim
NamePart
(type = given)
Sinae
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Sinae Kim
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Hoover
NamePart
(type = given)
Donald
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Donald Hoover
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2012
DateOther
(qualifier = exact);
(type = degree)
2012-10
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
In physical and mental health research areas, longitudinal studies are popular tool for addressing outcome changes over time within and between individuals. However, monotone-type missing data caused by dropout is unavoidable in many longitudinal studies and may lead to biased inference and incorrect conclusions if the nature of dropout is ignored. The dropout process may cause three types missingness: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). Most of existed statistical methods have treated the entire dropout the same, although this assumption may not be true in practice. For a real longitudinal study, based on observed dropout reasons, it may be more realistic to assume the nature of missingness to be a mixture of MCAR, MAR and MNAR. In this thesis, two approaches are proposed to deal the mixed nature of dropout due to different longitudinal dataset’s pattern and assumptions, and both of them are based on Pattern Mixture Model. If the outcome of interest can be assumed to follow a linear trend over time and the detailed dropout reasons for each subject are unknown, EM algorithm method will be added to Pattern Mixture Model to reflect a mixture of missing natures. If the outcome of interest is not linear with respect time and the detailed reasons for each dropout are known, available-case missing value restriction and non-future-dependent missing value restriction will be used within Pattern Mixture Model to identify the distribution of unknown measurements caused by different dropout reasons. Multiple imputation method will be combined to impute multiple complete datasets to reflect the uncertainty caused by missing values.
Subject
(authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_4335
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xii, 122 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Jing Gong
Subject
(authority = ETD-LCSH)
Topic
Longitudinal method
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066750
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T37H1HDD
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Gong
GivenName
Jing
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2012-09-30 18:12:34
AssociatedEntity
Name
Jing Gong
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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