STEP BY STEP APPROACH TO
EVALUATION AND COMPARISON
OF ANALYTICAL METHODS
J M KUYL
Department of Chemical Pathology
NHLS Universitas & UFS
Physicists have a long tradition of building their own
equipment, and are often fascinated by its
mechanics. Biologists’ fascination is primarily with
the mechanics of nature and, for many, the
machines themselves are simply tools –
complicated ‘black boxes’ that produce the results
they need. It doesn’t help that the tools biologists
are using may have been designed by physicists,
and that the two groups tend to use different jargon.
Nature 2007; 447: 116
INTRODUCTION
• Quantitative analytical methods have become more
reliable and more standardized.
• Emphasis moved away from methods development to
the selection and evaluation of those commercial
available methods that suit a particular laboratory best.
• Commercial kit methods are ready for implementation in
the laboratory, often in a “closed” analytical system on a
dedicated instrument.
• Furthermore, method evaluation is a costly exercise in
terms of reagents, specimens, and labour and time of
the professionals doing the evaluating.
• If not done properly it wastes laboratory revenue and
time, if the method is accepted might lead to errors in
medical decisions based on results the method
generates on patient samples.
Generally what happens is that
laboratories are most concerned with
getting the methods up and running that
there is little time, or thought given, to
selection and evaluation studies.
• The most common scenario is the
implementation of readily available commercial
kit methods, often in a “closed” analytical system
on a dedicated instrument.
• When a new clinical analyzer is included in the
overall evaluation process, various instrumental
parameters also require evaluation. Information
on most of these parameters should be available
from the instrument manufacturer, who should
also be able to furnish information on what user
studies to conduct in estimating these
parameters for an individual analyzer.
Establish need
Method selection
Definition of quality
goal
Method
evaluation
Method
development
Implementation
Submission of
specimen
Routine
analysis
Result
report
Quality control
practices
Reasons for Selecting a New Method
•
improve accuracy and / or precision over
existing methods
•
to reduce reagent cost
•
to reduce labour cost
•
new analyzer or instrument
•
to measure a new analyte
METHOD SELECTION
•
•
•
•
Evaluation of need
Application characteristics
Method characteristics
Analytical performance characteristics
Scopes of Method Evaluation
Studies
• Evaluation is the determination of the analytical performance
characteristics of a new method.
• Validation is confirmation by examination and provision of
objective evidence that the particular requirements for a specific
intended use can be consistently fulfilled.
• Verification is confirmation by examination of objective evidence
that specified requirements have been fulfilled.
• Demonstration is a minimum evaluation for a laboratory to use
to show that it is able to obtain expected results by following the
manufacturer’s instructions. This is appropriate for test systems
whose performance characteristics have been well studied and
documented.
Method Evaluation and Validation
• Main purpose is error assessment.
• To demonstrate that prior to reporting patient
test results, it can obtain the performance
specifications for accuracy, precision, and
reportable range of patient test results,
comparable to those established by the
manufacturer.
• The laboratory must also verify that the
manufacturer’s reference range is appropriate
for laboratory’s population.
An Overview of Qualitative Terms and Quantitative Measures Related
to Method Performance
Qualitative Concept
Quantitative Measure
Trueness
Closeness of agreement of mean value
with “true value”
Bias
A measure of the systematic error
Precision
Repeatability (within run)
Intermediate precision (long term)
Reproducibility (interlaboratory)
Imprecision (sd)
A measure of the dispersion of random
errors
Accuracy
Closeness of agreement of a single
measurement with “true value”
Error of measurement
Comprises both random and systematic
influences
Total Analytical error TEA.
TEA = RE + SE
RE
SE
TEA
Constant and proportional errors.
Analytical Sensitivity
• Several terms describe the different aspects of the
minimum analytical sensitivity of a method.
• Limit of absence (LoA) is the lowest concentration of
analyte that the method can differentiate from zero.
• Limit of detection (LoD) is the minimum concentration
of analyte whose presence can be quantitatively
detected under defined conditions.
• Functional sensitivity or limit of quantification (LoQ)
is the minimum concentration of analyte whose presence
can be quantitatively measured reliably under defined
conditions.
The concentration at which the CV = 20%.
Illustration of different aspects of analytical sensitivity
or detection limits.
Random Analytical Error (RE)
Factors contributing to random analytical error (RE) are those
that affect the reproducibility of measurement. These include:
• instability of the instrument,
• variations in the temperature,
• variations in the reagents and calibrators (and calibrationcurve stability),
• variability in handling techniques such as pipetting, mixing,
and timing, and
• variability in operators.
These factors superimpose their effects on each other at
different times. Some cause rapid fluctuations, and others
occur over a longer time. Thus RE has different components
of variation that are related to the actual laboratory setting.
Random Analytical Error (RE)
Components
• Within-run component of variation (wr)
• Within-day, between-run variation (br)
• Between-day component of variation (bd)
Within-run component of variation
(wr)
is caused by specific steps in the procedure:
1. sampling
2. pipetting precision
3. short-term variations in temperature and
4. stability of the instrument.
Within-day, between-run variation
(br)
is caused by:
1. instability of calibration curve
2. differences in recalibration that occur
throughout the day,
3. longer term variations in the instrument,
4. small changes in the condition of the
calibrator and reagents,
5. changes in the condition of the laboratory
during the day, and
6. fatigue of the laboratory staff.
Between-day component of variation (bd)
is caused by:
1. daily variations in the instrument,
2. changes in calibrators and reagents
(especially if new vials are opened each day),
and
3. changes in staff from day to day.
4. Although not a true random component of
variation, any drift in the stability of the
calibration curve over time greatly affects the
bd as well.
Total Variance of a Method
t2 = wr2 + br2 + bd2
RE = t
(t2)
Familiarization with the method
• It is essential that operators of the method become
thoroughly familiar with the details of the method and
instrument operation before the collection of any data
that will be used to characterize the method’s
performance.
• May include training by the manufacturer.
• It should be of sufficient duration that, at its completion,
the operators can perform all aspects of the method or
instrument operation comfortably.
Experiments for Estimating Analytical Errors
Outliers
The importance of daily examination and plotting of
comparison-of-method data cannot be over emphasized,
and the data must be carefully examined for outliers.
Definition of an outlier from a regression line:
| yi – Yi| > 4•sx,y
Outlier specimens must be detected immediately and
reanalyzed by both methods so that the data can correct
or confirm the outlier.
An example evaluation study: Cholesterol in serum.
Step 1: Analytical needs
Rapid procedure with a turnaround time of  30 min
suitable for lipid clinic requirement. Short turnaround
time means that patients do not have to come back for
treatment based on lipid-profile results.
A sample volume of  200 µL.
Analytical range of 0 to 20 mmol/L.
High through-put.
Analytical goals
An example evaluation study: Cholesterol in serum.
Analytical Goals
Analyte
Acceptable
performance
criteria
(CLIA 88)
Decision level
XC
Allowable
error
(CLIA 88)
Maximum
sd
(CLIA 88)
(CV%)
Medically based
maximum sd
(Fraser)
(CV%)
Albumin
± 10%
35 g/L
3.5
0.9 (2.6%)
0.5 (1.43%)
Cholesterol
± 10%
5.2 mmol/L
0.52
0.13 (2.5%)
0.14 (2.7%)
Creatinine
± 15%
88 µmol/L
265 µmol/L
26
40
7.0 (8%)
9.7 (3.7%)
1.8 (2.0%)
6.2 (2.3%)
± 10%
2.75 mmol/L
6.9 mmol/L
11.0 mmol/L
0.33
0.69
1.10
0.08 (2.9%)
0.18 (2.6%)
0.28
(2.55%)
0.06 (2.2%)
0.15 (2.2%)
0.24 (2.2%)
7.0%
0.35%
0.14%
± 0.5 mmol/L
3.0 mmol/L
6.0 mmol/L
0.50
0.50
0.12 (4%)
0.12 (2%)
0.07 (2.33%)
0.14 (2.33%)
ALP
± 30%
150 U/L
45
11 (7.3%)
5.1 (3.4%)
CK
± 30%
200 U/L
60
15 (7.5%)
40 (20%)
Glucose
Hb A1C
K
An example evaluation study: Cholesterol in serum.
Step 2: Quality goals
Medical decision (XC) levels of interest for cholesterol analysis
are taken as 4.5 mmol/L; levels below this indicate low risk of
CVD, and 6.0 mmol/L; high risk, levels above this should be
actively treated with cholesterol lowering drugs, respectively.
Precision goals for cholesterol are defined to be 0.12 mmol/L at
4.5 mmol/L and 0.15 mmol/L at 6.0 mmol/L (2.5%).
Total error goals (TEA) are 0.45 mmol/L at 4.5 mmol/L and 0.60
mmol/L at 6.0 mmol/L (10%).
Total Analytical error. (TEA)
For Cholesterol
TEA = RE + SE
10% = 2.5% + 7.5%
RE = 2.5%
SE = 7.5%
TEA = 10%
An example evaluation study: Cholesterol in serum.
Step 3: Method selection
Existing laboratory analyzer Beckman-Coulter LX20
analyzer
Cholesterol kit specifically designed for this analyzer.
Senior operator who is familiar with this particular analyzer
and is available to do the evaluation.
An example evaluation study: Cholesterol in serum.
Step 4: Test material selection
QC-material
Synchron 1: mean [cholesterol] 2.71 mmol/L,
Synchron 2: mean [cholesterol] 4.19 mmol/L, and
Synchron 3: mean [cholesterol] 5.82 mmol/L.
Pooled patient serum two levels A and B – matrix closest to
real patient serum.
20 Patient serum samples to be run in parallel with existing
laboratory method.
An example evaluation study: Cholesterol in serum.
Step 5: Within-run imprecision
Performed by analyzing 6 aliquots of Synchron 1, 2, and
3 and Pool A and B within a run.
Results:
Mean (mmol/L)
sd (mmol/L)
RE %
Synchron 1
2.69
0.028
1.04
Synchron 2
4.21
0.042
1.00
Synchron 3
5.80
0.073
1.26
Pool A
4.89
0.057
1.17
Pool B
6.54
0.109
1.67
An example evaluation study: Cholesterol in serum.
Step 5a: Within-run imprecision
Testing for acceptable performance
RE against Maximum allowable CV%
CLIA 88: 2.5% > synchron 1: 1.04% < Fraser: 2.7%
CLIA 88: 2.5% > synchron 2: 1.00% < Fraser: 2.7%
CLIA 88: 2.5% > synchron 3: 1. 26% < Fraser: 2.7%
CLIA 88: 2.5% > pool A: 1.17% < Fraser: 2.7%
CLIA 88: 2.5% > pool B: 1.67% < Fraser: 2.7%
proceed with step 5b
An example evaluation study: Cholesterol in serum
Step 5b: Within-run imprecision
Testing for acceptable performance
RE against TEA
If 4 x RE > TEA reject method
If 4 x RE < TEA proceed with step 6
With the TEA = 10% for cholesterol, the within-run imprecision
of synchron 1, 2, 3 and pool A and B each passes the test.
Proceed to step 6.
An example evaluation study: Cholesterol in serum.
Step 6: Between-run (day-to-day) precision
Performed by analyzing aliquots of pool A and B for 20 days
Results
Mean
sd
(mmol/L) (mmol/L)
RE %
4 x RE%
Pool A
4.93
0.098
1.99 < 2.5
7.96 < 10
Pool B
6.49
0.135
2.08 < 2.5
8.32 < 10
An example evaluation study: Cholesterol in serum
Step 7: SD has confidence intervals
Factors for computing one-sided confidence intervals
for standard deviation.
Degrees of
freedom (N – 1)
A0.05
A0.95
1
0.5103
15.947
5
0.6721
2.089
10
0.7391
1.593
15
0.7747
1.437
20
0.7979
1.358
An example evaluation study: Cholesterol in serum
Step 7: Confidence-interval estimate of random error REU
and REL ; N = 20
sd
sdU=
sdL=
REU= REL=
sd x A.95 sd x A.05 4 x sdU 4 x sdL
Mean
(mmol/L)
(mmol/L)
Pool
A
4.93
0.098
0.133
0.078
0.532
0.312
Pool
B
6.49
0.135
0.183
0.108
0.732
0.432
REU pool A > 0.493 and REU pool B > 0.649
An example evaluation study: Cholesterol in serum
Step 8: Validation of linearity or reportable range
Obtained pool C by combining all serum samples with
[cholesterol] > 15 mmol/L.
Prepared the following samples:
Sample 1
Special prepared with [cholesterol]  0
Sample 2
3 parts sample 1 + 1 part pool A
Sample 3
Pool A
Sample 4
Pool B
Sample 5
2 parts sample 1 + 2 parts pool C
Sample 6
Pool C
An example evaluation study: Cholesterol in serum
Step 8: Validation of linearity or reportable range
Pools analyzed by Kendal-Abell method (reference method)
[cholesterol]
mmol/L
Pool A
4.88
Pool B
6.52
Pool C
16.7
An example evaluation study: Cholesterol in serum
Step 8: Validation of linearity or reportable range
Samples 1, 2, 3, 4, 5, and 6 were analyzed in triplicate in a single run in
random order.
Theoretical (X)
Mean (Y)
Bias (%)
Sample 1
0
0.035
+0.035 (N/A)
Sample 2
1.22
1.967
-0.024 (-2.0)
Sample 3
4.88
4.846
-0.034 (-0.7)
Sample 4
6.52
6.47
-0.05 (-0.77)
Sample 5
8.09
7.99
-0.1 (-1.24)
Sample 6
16.7
16.35
-0.35 (-2.1)
Method (Y) [cholesterol] mmol/L
Reportable Range of Serum-[cholesterol]
20
Y = 0.9565 X + 0.3125
R = 0.9989
15
10
5
0
0
5
10
15
Theoretical (X) [cholesterol] mmol/L
20
An example evaluation study: Cholesterol in serum
Step 9: Estimation of SE from the linearity study which is a
comparison of the method against reference method.
The following statistics were obtained by linear
regression analysis:
Y = 0.956 X + 0.313 mmol/L SY,X = 0.294
Mean X = 6.235
Mean Y = 6.276
Bias = | mean Y – mean X| = 0.041 mmol/L
This is the estimate of SE at the mean of the data.
An example evaluation study: Cholesterol in serum
Step 9: Point estimate of SE at medical decision levels (XC).
For XC = 4.5 mmol/L, YC = 4.615 mmol/L
SE1 = | YC – XC | = 0.115 mmol/L
Because SE1 < TEA = 0.45 mmol/L,
SE1 is acceptable.
For XC = 6.0 mmol/L, YC = 6.049 mmol/L
SE2 = | YC – XC | = 0.049 mmol/L
Because SE2 < TEA = 0.6 mmol/L,
SE2 is acceptable
An example evaluation study: Cholesterol in serum
Step 10: Point estimate of TE
Criteria for acceptable performance:
TEA > TE = 3 x sd + | YC – XC |
For XC1 = 4.5 mmol/L, YC1 = 4.615 mmol/L and sd = 0.098
TE1 = 3 x 0.098 + 0.115 = 0.409 mmol/L < 0.45 mmol/L
Performance acceptable
For XC2 = 6.0 mmol/L, YC2 = 6.049 mmol/L and sd = 0.135
TE2 = 3 x 0.135 + 0.049 = 0.454 mmol/L < 0.6 mmol/L
Performance acceptable
An example evaluation study: Cholesterol in serum
Step 11: Medical decision chart
XC1
XC2
Level mmol/L
4.5
6.0
TEA mmol/L
0.45
0.60
SE mmol/L
0.115
0.049
RE mmol/L
0.098
0.135
RE as % of TEA
21.8
22.5
SE as % of TEA
25.6
8.2
Medical decision Chart
 XC1
 XC2


Use of method decision chart.
A method with:
1. Unacceptable performance does not meet the requirement for
quality, even when the method is working properly. Not acceptable
for routine operation.
2. Marginal performance provides the desired quality when everything
is working correctly. But, difficult to manage in routine operation,
requires total QC strategy, well-trained operators, aggressive
preventive maintenance, etc.
3. Good performance meets requirement for quality and can be wellmanaged in routine service. Requires multirule procedure with 4-6
control measurements per run.
4. Six sigma or excellent performance is clearly acceptable and easy
to manage in routine service and can be controlled
A comparison of methods experiment is performed to estimate
inaccuracy or systematic error.
This performed by analyzing patient samples by the new method
(test method) and a comparative method, then estimate the
systematic errors (SE) on the basis of differences observed
between the methods.
The systematic differences at the critical medical decision
concentrations are the errors of interest.
When possible, a “reference method” should be chosen for the
comparative method.
Any differences between a test method and a reference method
are assigned to the test method.
Cholesterol Methods Comparison Plot. N = 20
Test Method mmol/L
14
12
y = 1.0032x - 0.0233
R = 0.999
10
8
6
4
2
0
0
2
4
6
8
10
Comparative Method mmol/L
12
14
Bland - Altman Difference Plot
3
% Difference
2
1
0
-1 0
5
10
-2
-3
[Cholesterol] mmol/L
15
Interpretation of comparison of
methods study.
The differences are relatively small, not more than
2.2% across the concentration range of 2.0 –
15.0 mmol/L.
The two methods have the same relative accuracy.
The can be substituted for the other.
Recommended Minimum Studies for comparison
of methods experiment.
1. Select 40 patient specimens to cover the full working
range of the method.
2. Analyze 8 specimens a day within 2 hours by the test
and comparative methods.
3. Graph results immediately on a difference plot and
inspect for discrepancies.
4. Reanalyze specimens that give discrepant results.
5. Continue the experiment for 5 days if no discrepant
results are observed.
Recommended Minimum Studies for comparison of
methods experiment.
6. Continue for another 5 days if discrepancies are
observed during the first 5 days.
7. Prepare a comparison plot of all the data to assess the
range, outliers, and linearity.
8. Calculate the correlation coefficient and if 0.99 or
greater, calculate simple linear regression statistics and
estimate the systematic error at medical decision
concentrations.
9. Use the medical decision chart to combine the estimates
of SE and RE and make judgment on the total error
observed for the method.
NATURE, 18 September 2003
• Monkeys reject unequal pay.
- Sarah Brosnan and Frans de Waal
• Working for peanuts.
- Paul Smaglik