Document Detail


Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.
MedLine Citation:
PMID:  18985706     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example.
Authors:
Pang Du; Liansheng Tang
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  28     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2009 Jan 
Date Detail:
Created Date:  2008-12-16     Completed Date:  2009-04-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  349-59     Citation Subset:  IM    
Copyright Information:
Copyright (c) 2008 John Wiley & Sons, Ltd.
Affiliation:
Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
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MeSH Terms
Descriptor/Qualifier:
Bias (Epidemiology)
Diagnostic Tests, Routine / standards,  statistics & numerical data
Empirical Research
False Positive Reactions
ROC Curve*
Statistics, Nonparametric*

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


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