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Reducing cost in sequential testing: a limit of indifference approach.
MedLine Citation:
PMID:  23339070     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In noninferiority studies, a limit of indifference is used to express a tolerance in results such that the clinician would regard such results as being acceptable or 'not worse'. We applied this concept to a measure of accuracy, the Receiver Operating Characteristic (ROC) curve, for a sequence of tests. We expressed a limit of indifference for the range of acceptable sensitivity values and examined the associated cost of testing within this range. In doing so, we generated the minimum cost maximum ROC (MCMROC) curve, which reflects the reduced sensitivity and cost of testing. We compared the MCMROC and its associated cost curve between limits of indifference set to 0.999 [a 0.1% reduction in true positive rate (TPR)], 0.95 (a 5% reduction in TPR), and 1 (no reduction in TPR). The limit of indifference tended to have less of an effect on the MCMROC curves than on the associated cost curves that were greatly affected. Cost was reduced at high false positive rates (FPRs) at higher limit of indifference (0.999) and at small FPRs as the limit of indifference decreased (0.95). These patterns were also observed as applied to sequential strategies used to diagnose diabetes in the Pima Indians. Copyright © 2013 John Wiley & Sons, Ltd.
Authors:
Anwar E Ahmed; Christine M Schubert; Donna K McClish
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-22
Journal Detail:
Title:  Statistics in medicine     Volume:  -     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-22     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2013 John Wiley & Sons, Ltd.
Affiliation:
Department of Epidemiology and Biostatistics, College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
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