Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_3946
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
x, 82 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Dungang Liu
Abstract (type = abstract)
This dissertation develops efficient statistical methodologies for combining information
from independent sources. The developments focus on two settings where the
studies are heterogeneous or the studies involve rare events. In these settings, the conventional
combining approaches often lead to inefficient or even invalid statistical inference.
In this dissertation, we propose effective and efficient combining approaches
using confidence distributions. The proposed approaches are justified both theoretically
and numerically. They are also shown to be superior to the conventional approaches.
Combining information from multiple studies, often referred to as meta-analysis
in the literature, has been used extensively in many fields, including health sciences,
social sciences, and others. However, there remain many unresolved problems on how
to effectively and efficiently combine information. For example, • Heterogeneous studies – When the effect of interest is not estimable in heterogeneous studies (e.g., indirect evidence), how can we utilize these studies to perform meta-analysis? • Rare events studies – For clinical trials with rare events, how can we perform meta-analysis and incorporate studies with zero events in the analysis without using artificial continuity corrections or relying on large sample theory? To address these challenging but recurrent problems, this dissertation develops new
meta-analysis approaches based on combining confidence distributions. Roughly speaking,
a confidence distribution refers to a sample-dependent distribution function on
the parameter space with desirable inferential properties in terms of repeated sampling
performance. It can be viewed as a frequentist counterpart to the posterior distribution
in Bayesian inference. In this dissertation, we show the combination of confidence distributions
has desirable properties which are lacking in the conventional approaches. Specifically, 1) in the presence of heterogeneous studies, the proposed approach integrates direct and indirect evidence and achieves asymptotic efficiency; 2) for rare event studies, the proposed approach yields exact inference and incorporates all the studies in the analysis without using artificial continuity corrections for zero events. These properties are demonstrated numerically in simulation studies and real data examples, including flight landing safety data collected by the Federal Aviation Administration and drug safety data collected in diabetes clinical trials.
Rutgers University. Graduate School - New Brunswick
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License
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Author Agreement License
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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.