Clinical Biochemistry of Domestic Animals (Sixth Edition) - UMK ...

10
Chapter | 1 Concepts of Normality in Clinical Biochemistry
Sensitivity
0.00 0.25 0.50 0.75 1.00
0.00
0.25 0.50 0.75 1.00
1 Specificity
Type III ROC area: 0.9568 Type I ROC area: 0.9873
Reference
FIGURE 1-8 ROC curve comparison of the performance of
glucose in distinguishing between normal dogs and dogs with type III
diabetes mellitus and between normal dogs and dogs with type I diabetes
mellitus.
This probability can be obtained as output from statistical
software programs that perform ROC analysis such as
STATA for Windows Release 9.2.
2
As an example, we compare the performance of glucose
in distinguishing between normal dogs and type III
dogs and between normal dogs and type I dogs. A hundred
random glucose responses were drawn from each of
the type III and type I dog populations, and 1000 random
glucose responses were drawn from the normal dog population.
A STATA data file was made consisting of three
columns. The first column (labeled type III ) contained the
100 glucose responses from the type III dog population
followed by the 1000 normal responses and the second column
(labeled type I ) contained the 100 glucose responses
from the type I dog population followed by the 1000 normal
responses. The third column (labeled State ) contained
“ 1 ” in the first 100 cells and “ 0 ” in the remaining 1000
cells indicating the true population membership (abnormal
or normal) corresponding to the dogs in each of the first
and second columns. The ROC analysis can be obtained
using the following commands:
[ Graphics (from the main menu of STATA) → Roc
analysis → Compare ROC curves. Within the Roccomp
dialog box, select State as the Reference variable, type
III as the Classification variable and type I as the only
Additional classification variables. Finally, select Graph
the ROC curves and Report the area under the ROC
curves. Hit OK.]
Figure 1-8 gives the results of the ROC analysis. It
shows that glucose was a slightly better discriminator of
type I dogs and normals dogs (the area under the ROC
2
StataCorp, 4905 Lakeway Drive, College Station, TX 77845.
curve estimated to be 0.9873) than that of type III dogs and
normal dogs (the area under the ROC curve estimated to be
0.9568). This difference was marginally statistically significantly
( p 0.0345) using a chi-square test (not shown).
III . ACCURACY IN ANALYTE
MEASUREMENTS
Accuracy has to do with the conformity of the actual value
being measured to the intended true or target value. An
analytical procedure having a high level of accuracy produces
measurements that on average are close to the target
value. An analytical procedure having a low level of accuracy
produces measurements that on average are a distance
from the target value. Such a procedure in effect measures
something other than is intended and is said to be biased .
Failure of analytical procedures to produce values that on
average conform to the target values is due to unresolved
problems, either known or unknown, in the assay.
The degree of accuracy of an analytical procedure
has been difficult to quantify because the target value is
unknown. It is now possible for laboratories to compare
their assay results with definitive results obtained by the
use of isotope dilution-mass spectrometry ( Shultz, 1994 ).
Shultz (1994) reported the results of two large surveys
of laboratories in the United States ( Gilbert, 1978 ) and
Sweden ( Björkhem et al. , 1981 ) in which samples from
large serum pools were analyzed for frequently tested
analytes (calcium, chloride, iron, magnesium, potassium,
sodium, cholesterol, glucose, urea-nitrogen, urate, and
creatinine). The laboratory averages were compared with
the target value obtained using definitive methods, and the
results of these surveys indicated that, with the exception
of creatinine, all averages expressed as a percentage of the
target value were within the accuracy goals published by
Gilbert (1975) . Results from individual laboratories naturally
would vary about the average, and many of these laboratories
would not have met the accuracy goal.
IV . PRECISION IN ANALYTE
MEASUREMENTS
Precision has to do with how much variability there is
about the actual value being measured when the assay is
replicated. If in a given laboratory a particular assay is run
repeatedly on the same sample and the results obtained
have little variability, the assay is said to have high precision.
Large variability in the observed results indicates low
assay precision. Note that precision is defined in reference
to what is actually being measured and not to the target
value. Clinical analysts have always had a goal of achieving
the highest possible level of precision for a particular