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CLSI EP24

Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves, 2nd Edition

This document provides a protocol for evaluating the accuracy of a test to discriminate between two subclasses of subjects when there is some clinically relevant reason to separate them. In addition to the use of receiver operating characteristic curves and the comparison of two curves, the document emphasizes the importance of defining the question, selecting the sample group, and determining the "true" clinical state.

This document is available in electronic format only.

This reaffirmed document has been reviewed and confirmed as suitable to remain published without revision to content, as of April 2016. The document’s next scheduled review is generally five years after the reaffirmation date.

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Details

Chairholder: Martin H. Kroll, MD

Date of Publication: November 30, 2011

Order Code PDF: CLSI EP24A2E
ISBN Number: 1-56238-778-2

Order Code Print: print not available

Edition: Second

Pages: 56

CLSI EP24 Additional Details

If interested in ordering larger quantities of this document in print, please contact us here.

The U.S. Food and Drug Administration (FDA) has evaluated and recognized this approved-level consensus standard for use in satisfying a regulatory requirement.

This document was formerly sold under the code GP10.

CLSI EP24 Abstract

Clinical and Laboratory Standards Institute document EP24-A2—Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves; Approved Guideline—Second Edition provides guidance for laboratorians and manufacturers who assess clinical test accuracy. It is not a recipe; rather, it is a set of concepts to be used to design an assessment of test performance or to interpret data generated by others. In addition to the use of ROC curves and comparison of two curves, the document emphasizes the importance of defining the question, selecting a sample group, and determining the “true” clinical state. The statistical data generated can be useful whether one is considering replacing an existing test, creating or adding a new test, or eliminating a current test.