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Evaluating Diagnostic Tests

Name: Evaluating Diagnostic Tests
Creator: L. A. Thibodeaul
Keywords: diagnostic tests, 03 medical and health sciences, 0302 clinical medicine, Classification and discrimination; cluster analysis (statistical aspects), misclassification as nonsampling error, conditional probabilities of correct classification, specificity, classification error, 0101 mathematics, sensitivity

Evaluating diagnostic tests

descriptionPublicationkeyboard_double_arrow_right Article 01 Dec 1981Publisher:JSTORJournal:Biometrics, volume 37, page 801 (issn: 0006-341X,

Authors: L. A. Thibodeaul;

doi: 10.2307/2530161

Evaluating Diagnostic Tests

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Abstract

Diagnostic tests, regarded as methods of classification, are rarely perfect. Classification error and competing technologies require careful evaluation of diagnostic tests, usually by comparison to some standard. Conditional probabilities of correct classification, called 'sensitivity' and 'specificity', are typically used as evaluative measures. In this paper we consider misclassification as nonsampling error, evaluating its effect on the observed sensitivity and specificity.

Keywords

diagnostic tests, Classification and discrimination; cluster analysis (statistical aspects), misclassification as nonsampling error, conditional probabilities of correct classification, specificity, classification error, sensitivity, Applications of statistics to biology and medical sciences; meta analysis

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	88
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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