Document Detail


Regret graphs, diagnostic uncertainty and Youden's Index.
MedLine Citation:
PMID:  8783436     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Regret is the difference in outcome between the action we took and the best action we could, in retrospect, have taken. 'Tent graphs', representing decision problems under uncertainty in terms of expected regret, offer an instructive geometric supplement to standard formulae, allow instant calculations, and suggest, as we shall illustrate, novel algebraic properties. For instance, the problem of finding the pre-test probability range in which it is worthwhile doing a diagnostic test becomes trivial, even when test costs depend on the unknown disease status; we take the opportunity to explore the (non-trivial) algebraic properties of this problem. The graphs also provide a simple way to illustrate and read off the expected value of information and expected value of perfect information. This property is used to derive a clinical interpretation of Youden's Index (sensitivity + specificity - 1), namely, it is the maximum proportional reduction in expected regret achieved by the test. Finally, we examine the relationship between Youden's Index and the area under an ROC curve.
Authors:
J Hilden; P Glasziou
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  15     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  1996 May 
Date Detail:
Created Date:  1996-12-05     Completed Date:  1996-12-05     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  969-86     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, Panum Institut, Copenhagen N, Denmark.
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MeSH Terms
Descriptor/Qualifier:
Aged
Biometry
Decision Support Techniques*
Humans
Linear Models
Male
Models, Statistical
Probability*
ROC Curve
Sensitivity and Specificity

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


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