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

Missing Data in Meta-Analysis.

University of Rochester., Medicine and Dentistry.

FindBook

Google Book

Amazon

博客來

Missing Data in Meta-Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Missing Data in Meta-Analysis./
作者:	Chowdhry, Amit Kumar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	162 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
Contained By:	Dissertation Abstracts International77-08B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10038697
ISBN:	9781339549446

Missing Data in Meta-Analysis.
Chowdhry, Amit Kumar.

Missing Data in Meta-Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 162 p.

Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.

Thesis (Ph.D.)--University of Rochester, 2016.

This item is not available from ProQuest Dissertations & Theses.

Missing data is a problem seen throughout applied statistical analysis. Meta-analysis has many important applications throughout a wide variety of areas of research, including medicine, epidemiology, and the social sciences. Our review of the literature suggests that there exists a wide gap between state-of-the-art methods to accommodate missing data and current practice in meta-analysis. The widely used methodology in meta-analysis is only valid under very strict assumptions, and even then not necessarily as powerful as principled methods. This dissertation proposes multiple-imputation-based methods to handle missing sample variances, missing correlations from cross-over studies, and missing covariates in meta-regression. Our work has shown that the use of principled missing data methods will improve the practice of meta-analysis in the setting of missing data.

ISBN: 9781339549446Subjects--Topical Terms:

517247
Statistics.

Missing Data in Meta-Analysis.
LDR:01865nmm a2200313 4500 001 2161221
005 20180823122923.5
008 190424s2016 ||||||||||||||||| ||eng d
020 $a 9781339549446
035 $a (MiAaPQ)AAI10038697
035 $a (MiAaPQ)rochester:11131
035 $a AAI10038697
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chowdhry, Amit Kumar. $3 3349185
245 1 0 $a Missing Data in Meta-Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 162 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
500 $a Adviser: Michael P. McDermott.
502 $a Thesis (Ph.D.)--University of Rochester, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Missing data is a problem seen throughout applied statistical analysis. Meta-analysis has many important applications throughout a wide variety of areas of research, including medicine, epidemiology, and the social sciences. Our review of the literature suggests that there exists a wide gap between state-of-the-art methods to accommodate missing data and current practice in meta-analysis. The widely used methodology in meta-analysis is only valid under very strict assumptions, and even then not necessarily as powerful as principled methods. This dissertation proposes multiple-imputation-based methods to handle missing sample variances, missing correlations from cross-over studies, and missing covariates in meta-regression. Our work has shown that the use of principled missing data methods will improve the practice of meta-analysis in the setting of missing data.
590 $a School code: 0188.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
690 $a 0463
690 $a 0308
710 2 $a University of Rochester. $b Medicine and Dentistry. $3 3265809
773 0 $t Dissertation Abstracts International $g 77-08B(E).
790 $a 0188
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10038697