Document Detail

Nonparametric ROC summary statistics for correlated diagnostic marker data.
MedLine Citation:
PMID:  23055248     Owner:  NLM     Status:  MEDLINE    
We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We apply the proposed statistics to an endometriosis diagnosis study.
Liansheng Larry Tang; Aiyi Liu; Zhen Chen; Enrique F Schisterman; Bo Zhang; Zhuang Miao
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-10-11
Journal Detail:
Title:  Statistics in medicine     Volume:  32     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2013 Jun 
Date Detail:
Created Date:  2013-05-08     Completed Date:  2014-01-02     Revised Date:  2014-06-17    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  2209-20     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 John Wiley & Sons, Ltd.
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MeSH Terms
Area Under Curve
Computer Simulation
Diagnostic Imaging / methods*
Endometriosis / diagnosis
Longitudinal Studies
ROC Curve*
Statistics, Nonparametric
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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