Cardiovascular disease (CVD) is the most frequent cause of deaths worldwide [1]. Scien-tists have done and are stilling doing a high volume of research on this area, hoping to help people who are already suffering from the disease and also to prevent those at high risk of getting CVD. Statistical applications play a very important role in most of these research activities and a better utilization of the right statistical methodology for a specif-ic study would definitely make the research outcomes more reliable and eventually being beneficial to the human kind. This dissertation studies several scenarios in cardiovascular disease research where traditional statistical methods may not be applicable. And we pro-posed corresponding practical solutions or modifications to existing methods to better fit the problems case by case. In the first part, we are focusing on using the gain in life expectancy to assess the treat-ment effect of an antihypertensive therapy for stroke. We first propose a framework for estimating this quantity by calculating the area between estimated survival curves given by two comparative treatments. And then, in order to better assess the variability of our estimate especially with small sample size, we propose a new bootstrap method for ob-taining confidence interval for this quantity. We also propose the corresponding bootstrap testing procedure to test the null hypothesis. The second part of the dissertation is about meta-analysis in CVD research. We discover the non-normal behavior of the test statistics when sample size in each study of the meta-analysis is small. We use t distribution to approximate the underlying distribution and propose a simple formula to calculate the degree of freedom of the t distribution based on the sample size in each study as well as the number of studies. Finally, we modify a new clinical design called Simultaneous Global Drug Development Program (SGDDP) which can be more efficient for evaluating the treatment effect on diseases such as CVD where ethnicity have a potential impact. We add an additional as-sumption to the original test to make it unbiased. We also show the performance of the program after the modification.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5245
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xii, 90 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Kezhen Liu
Subject (authority = ETD-LCSH)
Topic
Cardiovascular system--Diseases--Research
Subject (authority = ETD-LCSH)
Topic
Cardiovascular system--Diseases--Statistics
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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.