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Parameter estimation from grouped data with applications to meta-analysis
Descriptive
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Text
TitleInfo
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Title
Parameter estimation from grouped data with applications to meta-analysis
Identifier
ETD_2619
Identifier
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000053102
Language
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eng
Genre
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theses
Subject
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Topic
Public Health
Subject
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(authority = ETD-LCSH)
Topic
Meta-analysis
Subject
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(authority = ETD-LCSH)
Topic
Linear models (Statistics)
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Goodness-of-fit tests
Abstract
(type = abstract)
Categorizing a continuous variable is easy for communication and statistical analysis in public health and medical research. However, categorization loses information, reduces statistical power, and biases the estimate of a dose-response association while reducing its efficiency. Further, it jeopardizes the validity and efficiency of a meta-analysis because of the single cutoff point and/or inconsistent cutoff points in the included studies. In order to appropriately summarize the estimates from each study in a meta-analysis with comparable categories or dose-response association, a new approach on re-estimating the underlying distribution of a categorized covariate by using the published information is the first step. This dissertation research proposes two types of approaches to estimate the underlying distribution. The first approach is linear model approach. When the underlying distribution follows a normal distribution, a linear model can be constructed by using the mean, standard deviation, and cutoff points with their cumulative probabilities in each study. The parameters can be estimated via the weighted mixed-effect linear regression model. When the underlying distribution follows a gamma distribution, a linear model is derived by applying a property of the incomplete gamma distribution. The parameters can be estimated by using a numerical iteration algorithm. The second approach is a goodness-of-fit approach. When the parameters of the underlying distribution cannot be linearized, based on the cutoff points and their cumulative probabilities in each study, the parameter estimates minimize the distance between the expected and observed values. We also applied this approach to estimate the parameters of a categorized zero-inflated distribution: the proportion of excess zero and the continuous variables. In addition, we discuss the impacts from categorization on the relative efficiency of estimating the parameters and the dose-response association, and the validity of the dose-response association by maximum likelihood approach via the multinomial distribution and simulation studies. In summary, the main contribution from this dissertation is that our approaches use published data to convert from the disadvantage of inconsistent cutoff points in many studies into useful information and to improve meta-analysis. We also generalize the approaches of evaluating the impacts from categorizing a continuous variable.
PhysicalDescription
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electronic resource
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xix, 222 p. : ill.
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Ph.D.
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Includes abstract
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Vita
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Includes bibliographical references
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by Yen-Hong Kuo
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Kuo
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Yen-Hong
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Yen-Hong Kuo
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Moore
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Dirk F
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chair
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Dirk F Moore
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LIN
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YONG
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YONG LIN
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Lu
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Shou-En
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Advisory Committee
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Shou-En Lu
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Davis
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John M
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outside member
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John M Davis
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Rutgers University
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Graduate School - New Brunswick
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2010
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2010
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Identifier
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doi:10.7282/T3BV7GP7
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Name
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Kuo
GivenName
Yen-Hong
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Permission or license
DateTime
2010-04-15 05:53:43
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Yen-Hong Kuo
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Rutgers University. Graduate School - New Brunswick
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2011-05-15
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365 days
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Yen-Hong Kuo
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Rutgers University. Graduate School - New Brunswick
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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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ETD
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