東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Development of Meta-Analysis in Biom...

Li, Lie.

FindBook

Google Book

Amazon

博客來

Development of Meta-Analysis in Biomedical Studies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of Meta-Analysis in Biomedical Studies./
作者:	Li, Lie.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	96 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275140
ISBN:	9781369866704

Development of Meta-Analysis in Biomedical Studies.
Li, Lie.

Development of Meta-Analysis in Biomedical Studies. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 96 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Southern Methodist University, 2017.

This research contains two topics: (1) Integrative Gene Set Enrichment Analysis Utilizing Isoform-Specific Expression; (2) Meta-Analysis of Rare Binary Events in Treatment Groups with Unequal Variability.

ISBN: 9781369866704Subjects--Topical Terms:

517247
Statistics.

Development of Meta-Analysis in Biomedical Studies.
LDR:04373nmm a2200337 4500 001 2156857
005 20180529081859.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9781369866704
035 $a (MiAaPQ)AAI10275140
035 $a (MiAaPQ)smu:10843
035 $a AAI10275140
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Lie. $3 3344633
245 1 0 $a Development of Meta-Analysis in Biomedical Studies.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 96 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Xinlei Wang; Guanghua Xiao.
502 $a Thesis (Ph.D.)--Southern Methodist University, 2017.
520 $a This research contains two topics: (1) Integrative Gene Set Enrichment Analysis Utilizing Isoform-Specific Expression; (2) Meta-Analysis of Rare Binary Events in Treatment Groups with Unequal Variability.
520 $a Gene Set Enrichment Analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool, which plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. Further, in the wake of next generation sequencing technologies, it has been made possible to measure genome-wide isoform-specific expression levels, calling for innovations that can utilize the unprecedented resolution. Currently, enormous amounts of data have been created from various RNA-seq experiments. All these give rise to a pressing need for developing integrative methods that allow for explicit utilization of isoform-specific expression, to combine multiple enrichment studies, in order to enhance the power, reproducibility and interpretability of the analysis. We develop and evaluate integrative GSEA methods, based on two-stage procedures, which, for the first time, allow statistically efficient use of isoform-specific expression from multiple RNA-seq experiments. Through simulation and real data analysis, we show that our methods can greatly improve the performance in identifying essential gene sets compared to existing methods that can only use gene-level expression.
520 $a Meta-analysis has been widely used to synthesize information from related studies to achieve reliable findings. However, in studies of rare events, the event counts are often low or even zero, and so standard meta-analysis methods such as fixed-effect models with continuity correction may cause substantial bias in estimation. Recently, Bhaumik et al. (2012) developed a simple average (SA) estimator for the overall treatment effect based on a random-effects (RE) model. They proved that the SA method with the continuity correction factor 0.5 (SA_0.5) is the least biased for large samples, and showed via simulation that it has superior performance when compared with other commonly used estimators. However, the RE models used in previous work are restrictive because they all assume that the variability in the treatment group is equal to or always greater than that in the control group. Under a general framework that explicitly allows treatment groups with unequal variability but assumes no direction, we prove that SA_0.5 is still the least biased for large samples. Meanwhile, to account for a trade-off between the bias and variance in estimation, we consider the mean squared error (MSE) to assess estimation efficiency and show that SA_0.5 fails to minimize the MSE. Under a new RE model that accommodates groups with unequal variability, we thoroughly compare the performance of various methods for both large and small samples via simulation, and draw conclusions about when to use which method in terms of both bias and MSE. A data example of rosiglitazone meta-analysis is used to provide further comparison.
590 $a School code: 0210.
650 4 $a Statistics. $3 517247
650 4 $a Health sciences. $3 3168359
650 4 $a Bioinformatics. $3 553671
690 $a 0463
690 $a 0566
690 $a 0715
710 2 $a Southern Methodist University. $b Statistical Science. $3 2102018
773 0 $t Dissertation Abstracts International $g 78-11B(E).
790 $a 0210
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275140