PMID- 24659835
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20140624
LR  - 20211021
IS  - 0159-8090 (Print)
IS  - 0159-8090 (Linking)
VI  - 35
IP  - 1
DP  - 2014 Feb
TI  - Uncertainty in measurement: a review of monte carlo simulation using microsoft 
      excel for the calculation of uncertainties through functional relationships, 
      including uncertainties in empirically derived constants.
PG  - 37-61
AB  - The Guide to the Expression of Uncertainty in Measurement (usually referred to as 
      the GUM) provides the basic framework for evaluating uncertainty in measurement. 
      The GUM however does not always provide clearly identifiable procedures suitable 
      for medical laboratory applications, particularly when internal quality control 
      (IQC) is used to derive most of the uncertainty estimates. The GUM modelling 
      approach requires advanced mathematical skills for many of its procedures, but 
      Monte Carlo simulation (MCS) can be used as an alternative for many medical 
      laboratory applications. In particular, calculations for determining how 
      uncertainties in the input quantities to a functional relationship propagate 
      through to the output can be accomplished using a readily available spreadsheet 
      such as Microsoft Excel. The MCS procedure uses algorithmically generated 
      pseudo-random numbers which are then forced to follow a prescribed probability 
      distribution. When IQC data provide the uncertainty estimates the normal 
      (Gaussian) distribution is generally considered appropriate, but MCS is by no 
      means restricted to this particular case. With input variations simulated by 
      random numbers, the functional relationship then provides the corresponding 
      variations in the output in a manner which also provides its probability 
      distribution. The MCS procedure thus provides output uncertainty estimates 
      without the need for the differential equations associated with GUM modelling. 
      The aim of this article is to demonstrate the ease with which Microsoft Excel (or 
      a similar spreadsheet) can be used to provide an uncertainty estimate for 
      measurands derived through a functional relationship. In addition, we also 
      consider the relatively common situation where an empirically derived formula 
      includes one or more 'constants', each of which has an empirically derived 
      numerical value. Such empirically derived 'constants' must also have associated 
      uncertainties which propagate through the functional relationship and contribute 
      to the combined standard uncertainty of the measurand.
FAU - Farrance, Ian
AU  - Farrance I
AD  - School of Medical Sciences, RMIT University, Bundoora, Victoria 3083;
FAU - Frenkel, Robert
AU  - Frenkel R
AD  - National Measurement Institute Australia, Bradfield Road, West Lindfield, NSW 
      2070, Australia.
LA  - eng
PT  - Journal Article
PT  - Review
PL  - Australia
TA  - Clin Biochem Rev
JT  - The Clinical biochemist. Reviews
JID - 8215797
PMC - PMC3961998
EDAT- 2014/03/25 06:00
MHDA- 2014/03/25 06:01
PMCR- 2014/02/01
CRDT- 2014/03/25 06:00
PHST- 2014/03/25 06:00 [entrez]
PHST- 2014/03/25 06:00 [pubmed]
PHST- 2014/03/25 06:01 [medline]
PHST- 2014/02/01 00:00 [pmc-release]
AID - cbr-35-37 [pii]
PST - ppublish
SO  - Clin Biochem Rev. 2014 Feb;35(1):37-61.