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Statistical applications to cardiova...

Liu, Kezhen.

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Statistical applications to cardiovascular disease research.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical applications to cardiovascular disease research./
作者:	Liu, Kezhen.
面頁冊數:	105 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3617237
ISBN:	9781303843822

Statistical applications to cardiovascular disease research.
Liu, Kezhen.

Statistical applications to cardiovascular disease research. - 105 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2014.

This item is not available from ProQuest Dissertations & Theses.

Cardiovascular disease (CVD) is the most frequent cause of deaths worldwide [1]. Scientists 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 specific 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 proposed corresponding practical solutions or modifications to existing methods to better fit the problems case by case.

ISBN: 9781303843822Subjects--Topical Terms:

1002712
Biostatistics.

Statistical applications to cardiovascular disease research.
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245 1 0 $a Statistical applications to cardiovascular disease research.
300 $a 105 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Adviser: Javier Cabrera.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2014.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Cardiovascular disease (CVD) is the most frequent cause of deaths worldwide [1]. Scientists 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 specific 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 proposed corresponding practical solutions or modifications to existing methods to better fit the problems case by case.
520 $a In the first part, we are focusing on using the gain in life expectancy to assess the treatment 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 obtaining confidence interval for this quantity. We also propose the corresponding bootstrap testing procedure to test the null hypothesis.
520 $a 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.
520 $a 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 assumption to the original test to make it unbiased. We also show the performance of the program after the modification.
520 $a [1] Global status report on noncommunicable diseases 2010. Geneva, World Health Organization, 2011.
590 $a School code: 0190.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
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710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Statistics and Biostatistics. $3 2094728
773 0 $t Dissertation Abstracts International $g 75-07B(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
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