Document Detail


Evaluation of diagnostic accuracy in detecting ordered symptom statuses without a gold standard.
MedLine Citation:
PMID:  21209155     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Our research is motivated by 2 methodological problems in assessing diagnostic accuracy of traditional Chinese medicine (TCM) doctors in detecting a particular symptom whose true status has an ordinal scale and is unknown-imperfect gold standard bias and ordinal scale symptom status. In this paper, we proposed a nonparametric maximum likelihood method for estimating and comparing the accuracy of different doctors in detecting a particular symptom without a gold standard when the true symptom status had an ordered multiple class. In addition, we extended the concept of the area under the receiver operating characteristic curve to a hyper-dimensional overall accuracy for diagnostic accuracy and alternative graphs for displaying a visual result. The simulation studies showed that the proposed method had good performance in terms of bias and mean squared error. Finally, we applied our method to our motivating example on assessing the diagnostic abilities of 5 TCM doctors in detecting symptoms related to Chills disease.
Authors:
Zheyu Wang; Xiao-Hua Zhou; Miqu Wang
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2011-01-05
Journal Detail:
Title:  Biostatistics (Oxford, England)     Volume:  12     ISSN:  1468-4357     ISO Abbreviation:  Biostatistics     Publication Date:  2011 Jul 
Date Detail:
Created Date:  2011-06-15     Completed Date:  2011-10-20     Revised Date:  2013-07-03    
Medline Journal Info:
Nlm Unique ID:  100897327     Medline TA:  Biostatistics     Country:  England    
Other Details:
Languages:  eng     Pagination:  567-81     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, University of Washington, Box 357232, Seattle, Washington 98195, USA. wangzy@u.washington.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Simulation
Data Interpretation, Statistical*
Diagnostic Techniques and Procedures / standards*
Humans
Likelihood Functions
Medicine, Chinese Traditional / methods*
Physicians
ROC Curve*
Grant Support
ID/Acronym/Agency:
F32GM085945/GM/NIGMS NIH HHS
Comments/Corrections

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


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