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Missing Data in Meta-Analysis.

Chowdhry, Amit Kumar.

Missing Data in Meta-Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Missing Data in Meta-Analysis./
Author:	Chowdhry, Amit Kumar.
Description:	1 online resource (162 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9781339549446

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

Missing Data in Meta-Analysis. - 1 online resource (162 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339549446Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Missing Data in Meta-Analysis.
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020 $a 9781339549446
035 $a (MiAaPQ)AAI10038697
035 $a (MiAaPQ)rochester:11131
035 $a AAI10038697
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Chowdhry, Amit Kumar. $3 1189221
245 1 0 $a Missing Data in Meta-Analysis.
264 0 $c 2016
300 $a 1 online resource (162 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Biostatistics. $3 783654
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0308
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Rochester. $b Medicine and Dentistry. $3 1183441
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10038697 $z click for full text (PQDT)

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