Verification of Performance
Specifications
An Advanced View of Method Validation
Version 5.0, August 2012
This project has been funded in whole or in part with Federal funds from the Division of AIDS (DAIDS), National
Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services,
under contract No. HHSN272201200009C, entitled NIAID HIV and Other Infectious Diseases Clinical Research
Support Services (CRSS).
Objectives

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2
Identify test classifications
Define what each validation experiment details for
testing methods
Discuss what is recommended to perform each of the
validation experiments for testing methods
Recognize how to evaluate data obtained from each of
the validation experiments
Pre-Assessment Question #1
A rapid Human Immunodeficiency Virus (HIV) test would
likely be classified as a:
A. High complexity, modified assay
B. Moderate complexity, unmodified assay
C. Food and Drug Administration (FDA)-approved,
modified assay
D. Waived, FDA-approved, unmodified assay
3
Pre-Assessment Question #2
The precision of a test method gives information related to
the method’s:
A.
B.
C.
D.
Systematic error
Comparison of results to a reference method
Reproducibility
Likelihood of being affected by hemolysis, lipemia and
icterus
E. Both A and B
4
Pre-Assessment Question #3
When transferring reference intervals of 20 specimens
used, what is the minimum number that must fall within
manufacturer’s reference intervals?
A.
B.
C.
D.
5
20
18
16
15
Pre-Assessment Question #4
Which linear regression equation component gives
information regarding constant bias?
A.
B.
C.
D.
6
y
x
m (slope)
b (intercept)
Selecting a Method
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7
Evaluate diagnostic tests
 Characteristics of testing
methods
 References: Technical literature
and manufacturer’s information
Select method of analysis
Validate method performance
Implement method
Perform tests with appropriate
Quality Control (QC) and External
Quality Assurance (EQA)
Method Validation
What is method validation?
Why must we validate?
When should we validate?
What should we validate?
8
Method Validation (cont’d)

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9
Why is validation important?
 Division of Acquired Immunodeficiency Syndrome
(DAIDS) requirement
How important is it that the results produced by the
testing method are reliable?
Shouldn’t the laboratory know the level of performance
of an adopted test method?
Tests to Validate
Waived
Non-waived
• Unmodified
FDA-approved
10
• Modified and/or
Non-FDA-approved
FDA Approval Resources
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11
Vendor
Publications
http://www.fda.gov/MedicalDevices/ProductsandMedical
Procedures/InVitroDiagnostics/LabTest/ucm126079.htm
Skill Check
What would you consider to be the complexity, per Clinical
Laboratory Improvement Amendments (CLIA), of the
glucose assay in the workbook?
A. Waived
B. Moderate
C. High
12
Skill Check
What would you consider to be the complexity of a rapid
urine pregnancy assay?
A. Waived
B. Moderate
C. High
13
Skill Check
What would you consider to be the complexity of
performing a manual white cell differential using a stained
whole blood smear?
A. Waived
B. Moderate
C. High
14
Method Validation
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15
Before you begin:
 Be sure you are familiar with the test method before
starting
 Know what to expect from the method (package insert,
discussions with technical assistance, and field service
representatives)
 Do not include results outside of stated reportable
ranges
 Predict your findings; establish limits/evaluation criteria
Terms for Discussion
Central Tendency
Dispersion
16
Values
Terms for Discussion (cont’d)
Run
17
Error in Test Methods
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
18
Some error is expected
 Examples
Error must be managed
 Understanding
 Defining specifications of allowable error
 Measurement
Total Error of Testing System
• CLIA Guidelines per analyte
• Other Guidelines
Systematic
Error
19
Random
Error
Total Error
Error Assessment
20
Systematic
Error
Random
Error
Total
Error
(SE)
(RE)
(TE)
In one direction,
cause results to
be high or low
In either
direction,
unpredictable
Combined
effect
Total Error Considerations
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21
Low End Performance Standards
 Recommendations derived from upper portion of
reportable range are more difficult to achieve at lower
concentrations
Maximum Total Error Allowed
 Considered to be 30% by David Rhoads, except for
amplification methods
Systematic and Random Errors
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22
Systematic Error
 Slope/Proportional error
 Intercept/Constant error
 Bias
Random Error
 Mean
 Standard deviation (SD)
 Coefficient of variation (CV)
Tools for Use
DataCrunching
Tools
Statistical
calculators,
graph paper
Spreadsheets
with
calculations
Validation
Software
(Westgard, AnalyzeIt, EP Evaluator)
23
How We Will Work Through This Module
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24
One quantitative test taken through the validation
process
One qualitative method taken through the validation
process
Reportable Range
Precision
Accuracy
Elements of
Validation
Reference Intervals
Sensitivity
Specificity
25
Precision
Introduction

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What is
needed
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
How we
perform
the testing
26

Definition: Reproducibility
Gives information related to random error
20 samples of same material (typically two
levels; e.g., Glucose at 50 and 300 mg/dL)
Standard solutions
Control materials
Pools (short term only)
Repeat testing over short and long term
(one day and 20 days, respectively)
Precision: How We Evaluate the Data
Calculate the following:



Mean
Standard deviation (SD)
Coefficient of Variation (CV)
What amount of random error is allowable,
based on CLIA criteria?
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27
Short term: 0.25 of allowable total error
Long term: 0.33 of allowable total error
Allowable Total Error Database
Link for:
 Clinical Laboratory Improvement Amendments (CLIA)
 College of American Pathologists (CAP)
 Royal College of Pathologists of Australasia (RCPA)
 Others
http://www.dgrhoads.com/db2004/ae2004.php
28
Values
Precision: Levey-Jennings (LJ) Charts
Run
29
Precision: How We Evaluate the Data
How do we compare to manufacturer’s data?
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30
Mean
SD
CV: More commonly used, allows for
easier comparison
Precision Example
Mean of Level 1 Glucose
90 mg/dL
CLIA Total Allowable Error
6 mg/dL or ± 10%
Total Allowable Error Level 1 Glucose
0.1 x 90 = 9 mg/dL
Random error allowed:
0.25 x total allowable
Short-term
precision
0.25 x 9 mg/dL
2.25 mg/dL
31
0.33 x total allowable
Long-term
precision
0.33 x 9 mg/dL
2.97 mg/dL
Activity
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
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32
Work with Levey-Jennings graph and data
Work with mean and standard deviation to calculate a
coefficient of variation, as well as a mean and a
coefficient of variation to calculate a standard deviation
Determine if precision data is acceptable
Accuracy
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Introduction


What is
needed
How we
perform the
testing
33
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Definition: How close to the true value
Comparison of methods
Gives information related to systematic error
Potential conflicts on interpretation of results
(reference values)
40 different specimens
Cover reportable range of method
Quality versus quantity
Duplicate measurements of each specimen
on each method
Minimum of five days, prefer over 20
(since replicate testing is same)
Accuracy: How We Evaluate the Data
Graph the Data:
Real time
34
Difference
plot
Comparison plot
Calculate
y = mx + b
Test method on Yaxis
b represents
constant error
Reference
(comparative)
method on X-axis
m represents
proportional error
Shows analytical
range of data,
linearity of response
over range and
relationship between
methods
Visual Inspection for Accuracy
Test Method
(x1, y1)
(x2, y2)
Slope = (y2- y1) / (x2- x1)
Intercept
Reference Method
35
Accuracy: How We Evaluate the Data
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36
Slope: Usually not significantly different from 1
Intercept: Not significantly different from 0
Significant difference with Medical Decision Points
Calculate Appropriate Statistics
Slope
 Measure of proportional bias
 m = (y1-y2)/(x1-x2) or “rise/run”
 Slope greater than 1 means the Y (Test) values are
generally higher than the X (Comparative) values
 Slope of 1.11 means the Y (Test) values are on
average 11% higher than the X (Comparative) values
37
Calculate Appropriate Statistics (cont'd)
Intercept of the Line
 Measure of constant bias between two methods
 Y (Test) value at the point where the line crosses the Y
axis
 If Y intercept is 12, then all Y (Test) values are at least
12 units higher than the X (Comparative) values
38
Accuracy
What type of bias do you see?
39
Accuracy (cont’d)
Constant Bias
40
Proportional Bias
Skill Check
Can a linear regression formula offer predictive value in
relation to method comparisons?
A. Yes
B. No
41
Activity
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42
Create graph based on sample set
Determine slope from best-fit line
Determine Y-intercept from best-fit line
Explain the relationship between comparative and test
results
Reportable Range / Linearity

Introduction

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What is
needed


How we
perform the
testing
43

Definition: Lowest and highest test results that
are reliable
Especially important with two point calibrations
Analytical Measurement Range (AMR) and
derived Clinical Reportable Range (CRR)
Series of samples of known concentrations
(e.g., standard solutions, EQA linearity sets)
Series of known dilutions of highly elevated
specimen or spiked specimens; EQA specimens
At least four levels (five preferred)
CLSI recommends four measurements of each
specimen; three are sufficient
Reportable Range:
How We Evaluate the Data
Plot mean values of:


Measured values on Y-axis versus
Known or assigned values on X-axis
Visually inspect, draw best-fit line, estimate
reportable range
Compare with expected values (typically
provided by manufacturer)
44
Reportable Range Activity
Assigned
Value
45
Experimental Results
Average
Rep #1
Rep #2
Rep #3
Rep #4
10.0
____
11.0
10.0
11.0
10.0
100.0
____
99.0
103.0
103.0
101.0
300.0
____
303.0
305.0
304.0
306.0
500.0
____
505.0
506.0
505.0
506.0
800.0
____
740.0
741.0
744.0
742.0
Reportable Range Activity (cont'd)
Assigned
Value
46
Experimental Results
Average
Rep #1
Rep #2
Rep #3
Rep #4
10.0
10.5
11.0
10.0
11.0
10.0
100.0
101.5
99.0
103.0
103.0
101.0
300.0
304.5
303.0
305.0
304.0
306.0
500.0
505.5
505.0
506.0
505.0
506.0
800.0
741.8
740.0
741.0
744.0
742.0
Reportable Range Activity (cont'd)
Linearity Scatter Plot
800.0
700.0
Recovered Values (Means)
600.0
500.0
400.0
300.0
200.0
100.0
0.0
0
100
200
300
400
500
As s igne d Conce ntrations (units )
47
600
700
800
900
AMR vs. CRR
Analytical Measurement Range (AMR)
Linearity
Clinically Reportable Range (CRR)
Allows for dilution or other preparatory steps
beyond routine
48
Skill Check
If you do not have enough specimen to perform a dilution,
upon which reportable range component must you rely?
A.
B.
C.
D.
49
AMR
CRR
Neither A or B
Both A and B
Linearity Materials
Utilizing the marketing materials from the two chemistry
linearity kits in your handouts:
1. Determine which kit would be more appropriate for
use with the chemistry assay you chose earlier
2. Explain your reasoning
50
Graph Activity
Given your choice of linearity kits, you perform your AMR
experiments by performing four replicates of each level of
known concentration solution. The data you obtain is
displayed on the next slide.
1. Review data; record any initial observations
2. Graph data on supplied graph paper
3. Determine your assay’s AMR
51
Linearity Experiment Results
52
Level
Rep 1
Rep 2
Rep 3
Rep 4
1
24
23
25
24
2
196
197
171
194
3
359
360
358
361
4
530
532
529
535
5
700
695
702
709
Activity
Using an Excel spreadsheet,
create a graph and calculate
linear regression statistics from
the data provided
53
54
Rep 1
Rep 2
Rep 3
Rep 4
Lab's
Average
Known
Conc
24
23
25
24
24
25
196
197
171
194
195.7
200
359
360
358
361
360
375
530
532
529
535
532
550
700
695
702
709
702
725
Recovered
AMR Verfication
800
700
600
500
400
300
200
100
0
0
200
400
Known Concentration
55
600
800
Dilution Protocols
56

Your medical director, in consultation with clinicians,
determines that for proper study participant care the
Clinically Reportable Range (CRR) for glucose is
15 – 1400 mg/dL

Given your linearity experiment results and the package
insert, devise a dilution protocol to be contained within
our Glucose SOP
Reportable Results
Given your AMR, CRR, and dilution protocol, how would
you handle the following analyzer results?
1. 12 mg/dL
2. 800 mg/dL
3. 1600 mg/dL
57
Reference Intervals

Introduction


What is
needed


How we
perform the
testing
58


Definition: Normal range in healthy population
Used for diagnosis/clinical interpretation of
results
Pre-defined “normal” criteria for screening
purposes
Transferring: 20 “normal” individuals’ specimens
Establishing: 120 “normal” individuals’ specimens
Perform testing on all samples
Document results
Reference Intervals:
How We Evaluate the Data
Transferring
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59
18 of 20 must
fall within manufacturer’s
ranges
Establishing


Calculate mean and SD
of data for each group
Reference Intervals =
mean ± 2 SD (if Gaussian
Distribution only, otherwise,
additional calculations
recommended)
Activity
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60
Determine if assay is eligible
for transference of reference
intervals
Review a sample set of data
to determine if transference
may be performed; if not,
determine next step(s)
Sensitivity

Introduction

What is
needed
How we
perform the
testing
61

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
Definition: Lowest reliable value; lower
limit of detection, especially of interest in
drug testing and tumor markers
Different terminologies used by different
manufacturers
Blank solutions
Spiked samples
20 replicate measurements over short or
long term, depending on focus
Sensitivity: How We Evaluate the Data
Three methods used:
Biological Limit of
Functional
Lower Limit of
Detection:
Sensitivity:
Detection (LLD):
LLD plus two or Mean concentration
Mean of the blank
sample, plus two or three times SD of for spiked sample
three SD of blank spiked sample with whose CV = 20%;
concentration of
lowest limit where
sample
detection limit
quantitative data is
reliable
62
Activity
Using the manufacturer’s
package inserts, find the
related information for
sensitivity. How was it
calculated?
63
Specificity

Definition: Determination of how well a method
measures the analyte of interest accompanied
by potential interfering materials

Introduction
64
What is
needed

Standard solutions, participant specimens
or pools
Interferer solutions (standard solutions, if
possible; otherwise, pools or specimens)
added at high concentrations
How we
perform the
testing

Duplicate measurements
Specificity: How We Evaluate the Data



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65
Tabulate results for pairs of
samples (dilution and interferent)
Calculate means for each (dilution
and interferent)
Calculate the differences
Calculate the average interference
of all specimens tested at a given
concentration of interference
Qualitative Assays


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66
Compare diagnosis
Assume comparative (reference) method is accurate
Determine the following:
 True Positives, True negatives
 False Positives, False negatives
Calculate sensitivity and specificity and compare to
manufacturer
Qualitative Assays: Control of Validation
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67
Negative and Positive Quality Controls
 Use QC materials recommended by manufacturer for
verification purposes
 Determine validity of other results, e.g., method
comparisons
 Evaluate failed runs if they occur during verification
process
Qualitative Methods: Precision
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
68
How is it performed?
 Runs of specimens with analyte concentrations near
the cutoff point
 Three specimens, one at cutoff, one just below cutoff,
and one just above cutoff (± 20% recommended)
 Replicate measurements of each of three specimens
(20 each, minimum)
How is it evaluated?
 Determine percentage of positives and negatives for
each specimen
 Evaluate cutoff, as well as other two specimens
Accuracy/Method Comparisons
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
69
How is it performed?
 Specimens typical of population (to be tested in future
use of method)
 50 positive specimens and 50 negative specimens
recommended; minimum 20 each
 Performed over 10 to 20 days
How is it evaluated?
 Discrepant results near cutoff?
 Most often sensitivity and specificity used to describe
performance
Qualitative Methods
Comparative or Reference
Method Result
True vs. False
Test
Method
Result
Positive
Negative


70
Positive
Negative
True Positive
False Positive
Positive Predictive
Value
False Negative
True Negative
Negative
Predictive
Value
Sensitivity
Specificity
False Positive Rate - False Positives divided by
total number of Negatives
False Negative Rate - False Negatives divided by
total number of Positives
Qualitative Methods (cont'd)
Comparative or Reference
Method Result
Test
Method
Result
Positive
Negative
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
71
Positive
Negative
True Positive
False Positive
Positive Predictive
Value
False Negative
True Negative
Negative
Predictive
Value
Sensitivity
Specificity
Sensitivity = 100 x True Positives divided by
(True Positives + False Negatives)
Specificity = 100 x True Negatives divided by
(True Negatives + False Positives)
Qualitative Methods (cont'd)
Comparative or Reference
Method Result
Test
Method
Result
Positive
Negative



72
Positive
Negative
True Positive
False Positive
Positive Predictive
Value
False Negative
True Negative
Negative
Predictive
Value
Sensitivity
Specificity
Predictive Values - Operation of a test on a mixed population
of Positive and Negatives
 A property of the test and the population; and affected by
prevalence of Positives
Positive Predictive Value = True Positives divided by
(True Positives + False Positives)
Negative Predictive Value = True Negatives divided by
(True Negatives + False Negatives)
Evaluation Criteria


73
High Diagnostic Value
 100% Sensitivity
 100% Specificity
What happens if True Positive rate is equal to the False
Positive rate?
Activity
Estimate sensitivity
and specificity of a
qualitative method
given a data set.
74
Activity (cont’d)
Create a validation
plan for a quantitative
assay to be performed
in your laboratory.
75
In Closing
Now that you have completed this module, you should be
able to:




76
Identify test classifications
Define what each validation experiment details for
testing methods
Discuss what is recommended to perform each of the
validation experiments for testing methods
Recognize how to evaluate data obtained from each of
the validation experiments
Post-Assessment Question #1
A rapid HIV test would likely be classified as a:
A.
B.
C.
D.
77
High complexity, modified assay
Moderate complexity, unmodified assay
FDA-approved, modified assay
Waived, FDA-approved, unmodified assay
Post-Assessment Question #2
The precision of a test method gives information related to
the method’s:
A.
B.
C.
D.
Systematic error
Comparison of results to a reference method
Reproducibility
Likelihood of being affected by hemolysis, lipemia and
icterus
E. Both A and B
78
Post-Assessment Question #3
When transferring reference intervals of 20 specimens
used, what is the minimum number that must fall within
manufacturer’s reference intervals?
A.
B.
C.
D.
79
20
18
16
15
Post-Assessment Question #4
Which linear regression equation component gives
information regarding constant bias?
A.
B.
C.
D.
80
y
x
m (slope)
b (intercept)
References










81
DAIDS Good Clinical Laboratory Practice (GCLP) Guidelines.
www.westgard.com.
Validation of Qualitative Methods.
42 CFR § 493.1253.
College of American Pathologists Commission on Laboratory Accreditation,
Accreditation Checklists, April 2006.
Westgard, James O. Basic Method Validation 2nd Edition. Madison, WI: Westgard
QC, Inc., 2003.
Clinical and Laboratory Standards Institute. User Protocol for Evaluation of
Qualitative Test Performance; Approved Guideline. NCCLS document EP12-A.
Clinical and Laboratory Standards Institute, Wayne, PA USA, 2002.
Clinical and Laboratory Standards Institute. Evaluation of Precision.
Performance of Quantitative Measurement Methods. NCCLS document EP5-A2.
Clinical and Laboratory Standards Institute, Wayne, PA USA, 2004.
Clinical and Laboratory Standards Institute. User verification of Performance for
Precision and Trueness. CLSI document EP15-A2. Clinical and Laboratory Standards
Institute, Wayne, PA USA, 2005.
Wrap Up
82