CLSI EP29

Expression of Measurement Uncertainty in Laboratory Medicine, 1st Edition

This guideline describes a practical approach to assist clinical laboratories in developing and calculating useful estimates of measurement uncertainty, and illustrates their application in maintaining and improving the quality of measured values used in patient care.

This document is available in electronic format only.

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Chairholder: Anders Kallner, MD, PhD

Date of Publication: January 31, 2012

Order Code PDF: CLSI EP29AE
ISBN Number: 1-56238-787-1

Order Code Print: print not available

Edition: First

CLSI EP29 Additional Details

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

A CLSI-IFCC joint project.

This document is included in the CLSI document code change and rebranding that is currently in process. The code will be changed from C51-A to EP29-A. Please make sure that the document title corresponds to the publication that you intend order.

This archived document is no longer being reviewed through the CLSI Consensus Document Development Process. However, this document is technically valid as of June 2018. Because of its value to the laboratory community, it is being retained in CLSI’s library.

CLSI EP29 Abstract

Clinical and Laboratory Standards Institute document EP29-A—Expression of Measurement Uncertainty in Laboratory Medicine; Approved Guideline describes the principles of estimating measurement uncertainty and provides guidance to clinical laboratories and in vitro diagnostic device manufacturers on the specific issues to be considered for implementation of the concept in laboratory medicine. This document illustrates the assessment of measurement uncertainty with both bottom-up and top-down approaches. The bottom-up approach suggests that all possible sources of uncertainty are identified and quantified in an uncertainty budget. A combined uncertainty is calculated using statistical propagation rules. The top-down approach directly estimates the measurement uncertainty results produced by a measuring system. Methods to estimate the imprecision and bias are presented theoretically and in worked examples.