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Some statistical methods for diagnos...

Yang, Yuqing.

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Some statistical methods for diagnostic accuracy with correlated data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Some statistical methods for diagnostic accuracy with correlated data./
Author:	Yang, Yuqing.
Description:	86 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3500.
Contained By:	Dissertation Abstracts International66-07B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3182990
ISBN:	9780542238994

Some statistical methods for diagnostic accuracy with correlated data.
Yang, Yuqing.

Some statistical methods for diagnostic accuracy with correlated data. - 86 p.

Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3500.

Thesis (Ph.D.)--Columbia University, 2005.

In this dissertation; we focus on the statistical analysis of diagnostic accuracy with correlated data. In Chapter 1, we review the literature on the related area. In Chapter 2, we propose a nonparametric approach for comparing diagnostic accuracies in multi-rater Receiver Operating Characteristic (ROC) studies. The approach constructs a test statistic froze each rater by extending the conventional nonparametric method and then combines all the individual test statistics to draw an overall conclusion on the relative accuracies of different diagnostic tests. The method can handle both continuous and ordinal data. Furthermore, the choice of the optimal weights in combining the individual test statistics is discussed. Compared to the existing nonparametric methods, the proposed method is robust and effectively deals with the possible heterogeneity among raters. The method is applied to a real example and its finite sample performance is examined through simulation studies.

ISBN: 9780542238994Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Some statistical methods for diagnostic accuracy with correlated data.
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020 $a 9780542238994
035 $a (UnM)AAI3182990
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100 1 $a Yang, Yuqing. $3 1912005
245 1 0 $a Some statistical methods for diagnostic accuracy with correlated data.
300 $a 86 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3500.
500 $a Adviser: Zhezhen Jin.
502 $a Thesis (Ph.D.)--Columbia University, 2005.
520 $a In this dissertation; we focus on the statistical analysis of diagnostic accuracy with correlated data. In Chapter 1, we review the literature on the related area. In Chapter 2, we propose a nonparametric approach for comparing diagnostic accuracies in multi-rater Receiver Operating Characteristic (ROC) studies. The approach constructs a test statistic froze each rater by extending the conventional nonparametric method and then combines all the individual test statistics to draw an overall conclusion on the relative accuracies of different diagnostic tests. The method can handle both continuous and ordinal data. Furthermore, the choice of the optimal weights in combining the individual test statistics is discussed. Compared to the existing nonparametric methods, the proposed method is robust and effectively deals with the possible heterogeneity among raters. The method is applied to a real example and its finite sample performance is examined through simulation studies.
520 $a In Chapter 3, a nonparametric approach for evaluating the diagnostic accuracy of a monitoring test has been proposed. In a monitoring program, patients are examined periodically with monitoring tests until the onset of the designated condition or the termination of the program. Therefore, different patients may contribute different numbers of measurements of a monitoring marker. The proposed method provides optimal estimates of diagnostic accuracy index in the monitoring test setting. The asymptotic properties of the estimates are discussed. Simulation studies are conducted to examine the finite sample performance of the proposed estimates. Future research opportunities are discussed in Chapter 4. Examples include the thoughts of improving ROC regression models.
590 $a School code: 0054.
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0308
710 2 0 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 66-07B.
790 1 0 $a Jin, Zhezhen, $e advisor
790 $a 0054
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3182990