Validation and verification of immunoassay methods dr. ali mirjalili

Validation and Verification of
Immunoassay methods
Dr. Ali Mirjalili
1398/01/30, 18 April 2019
The 12th international & national congress on quality
improvement in clinical laboratories , Tehran, Iran
Ali_Mirjalili@yahoo.com
a.Mirjalili@pishtazteb.com
Cellphone:+989123006798

Contents
2
1. Introduction to immunoassay
2. Standards
3. Terminology
4. Steps in method validation
5. Performance characteristics(Quantitative test validation)
Precision
Accuracy
Recovery
….
6. Qualitative test validation
7. Test Verification

Introduction
Laboratory Diagnosis based on:
Basic method (Culture,
Microscopy, etc)
Ag, Ab Detection
methods
(Immunoassay)
Nucleic Acid methods
(Amplification methods)

Classification of immunochemical
detection methods
• Precipitation
• Immunodiffusion
• Immunoelectrophoresis
• Agglutination
• Latex
• Hemagglutination
• Light scattering
• Nephelometry
• Turbidimetry
Non
label
methods
• Colorimetric
• Radiometric
• Chemiluminescent
• Fluorescent
Label
methods

Principle Colorimetric, Radiometric (RIA)
Chemiluminescent (CLIA),
Fluorescent (ELFA)

Overview
IVD Manufacturer Medical laboratory
Laboratory kit

In vitro diagnostics manufacturer; Regulations and
Standards
IVD Manufacturer

Medical labs; Regulations and Standards
Medical laboratory
Clinical and Laboratory Standards Institute (CLSI)
Standards and guidelines regarding clinical and laboratory
testing for use within the healthcare and medical testing
communities.

Medical laboratory
Medical labs; Regulations and Standards
• Clinical Laboratory Improvement Amendments (CLIA).
• Centers for Medicare &
Medicaid Services (CMS)
• The Food and Drug Administration (FDA),
• U.S. Department of
Transportation (DOT)
• Occupational Safety and
Health Administration, or OSHA
• Health Insurance Portability and Accountability Act of 1996, or
HIPAA

www.themegallery.com
ISO 13485 contents
Company Logo

ISO 15189 contents
Company Logo

Quantitative QC - Module 7 15
The Quality Management System
Organization Personnel Equipment
Purchasing
&
Inventory
Process
Control
Information
Management
Documents
&
Records
Occurrence
Management
Assessment
Process
Improvement
Customer
Service
Facilities
&
Safety

Five M of Quality
Man
Machine
FACILITY (SIZE, CONSTRUCTION, LOCATION)
Qualifications,
Organization,
Job description,
Training, etc.
Qualification, Calibration
Manual
Methodology
Material/
Sample
Storage
Label
Motivation
SOP, Mfr
Bruchure

Validation
• ISO 15189 standard in 2012
– requirements 5.5.1.2 and 5.5.1.3
• Validation Definition :
The validation shall be as extensive as is necessary and confirm,
through the provision of objective evidence (in the form of performance
characteristics), that the specific requirements for the intended use of
examination have been fulfilled.

Validation
Method validation: (Manufacturer concern)
Establishing the performance of a new
diagnostic tool.
Confirmation, through the provision of
objective evidence, that the requirements for
a specific intended use or application have
been fulfilled’ (doing correct test)......
ISO 9001:2005

Verification
• ISO 15189 standard in 2012
• 5.5.1 Selection, verification and validation of examination procedures
– requirements 5.5.1.2 and 5.5.1.3
• Definition :
Provision of objective evidence that a given item fulfills
specified requirements
the independent verification by the laboratory shall confirm, through obtaining
objective evidence
(in the form of performance characteristics) that the performance claims for the EP
have been met.
The performance claims intended use.

Verification
Method verification: (Lab / user concern)
A process to determine performance
characteristics before a
for patient testing.
test system is utilized
Confirmation,
objective
requirements
through
evidence,
the
that
provision of
specified
have been fulfilled’ (doing test
correctly)……ISO 9001:2005

Validation vs Verification
Verification Validation
When used without modification, the
validated EP shall be verified
 Non-standard methods
 Home made methods
 Modified validated methods or are
being used Outside their intended
scope.
 Presence of perennial problem by
quality control study.
 When the used method is changed by
adding new biological material.
Compare performance
characteristics
Define performance characteristics
User/MFQ MFQ / User

According to Westgard
The inner, hidden, deeper, secret meaning
of method validation is error assessment.
How much error might be present in the
test result within your laboratory ?
Could this degree of error affect the
interpretation and possibly patient care ?
If the potential error is large enough to lead to
misinterpretation,
acceptable.
then the method is not

Steps in Method Validation
 Define Goals
 Error Assessment
 Compare error vs. analytical goal

9
1st Step in Method Validation
Define Goals
 Accept that all lab measurements contain
experimental error
 What is an acceptable performance for:
–
–
–
–
Precision?
Accuracy?
Sensitivity?
Analytical measurement range?

11
2nd Step in Method Validation
Error Assessment
 Method validation assesses
–
–
–
Type of error
Magnitude of error
Clinical Significance of error
•
•
•
Literature guidelines
Physician input
Professional judgment

3rd Step in Method Validation
Compare error vs. analytical goal
Accept or reject your new method

Total Error of Testing System
30
• CLIA Guidelines per analyte
• Other Guidelines
Systematic
Error
Random
Error
Total Error
Constant,
Proportional

15
Total Analytical Error - TE
TE = RE + SE
RE
SE
X
- X

Quantitative QC - Module 7 32
Accuracy and Precision
Accurate = Precise but not Biased
Accurate
and Precise
Precise
but Biased
Imprecise,
Inaccurate
Imprecise,
Accurate

METHOD VALIDATION
Specificity
Mfr claimed
Performance
Characteristics
(Quantitative, Qualitative)
Reference
Interval Stability Known
Panels

Method Evaluation :New Method
1. Precision
2. Accuracy (measured bias) or comparability (measured
differences)
3. Linearity over the measuring interval or analytical
measurement range (AMR)
4. Limit of detection (LoD) and limit of quantitation (LoQ or
analytical sensitivity)
5. Analytical specificity or interference
6. Reagent or sample (analyte) carryover
7. Reference interval or decision value (interpretive
information)

CLSI and Evaluation Protocols
• EP05 Evaluation of Precision
• EP06 Evaluation of Linearity
• EP09 Evaluation of Bias and Comparability Using Patient Samples
• EP10 Preliminary Evaluation (Bias, Carryover, Drift, Linearity)
• EP12 User protocol for evaluation of qualitative test performance
• EP15 Verification of Precision and Trueness
• EP17 Limits of Detection and Limits of Quantitation
• C28 Defining, Establishing, and Verifying Reference Intervals

Measurement of Precision
1 2 3 4
5
Within lab
Ruggedness

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 (t
2)
t
2 = wr
2 + br
2 + bd
2
RE = t

Factors can change in Precision study
• Time
• Calibrator
• Operator
• Equipment

17
Random Error (RE) - Affects precision
• Estimated by:
1. Mean (=average(number1, number2, ….)
2. Standard deviation (SD) (=stdev(number1, nunmber2, …..)
3. Coefficient of variation (CV) (=SD/mean)

Accuracy
Closeness of determined value to the true value.
Represent Systemic Error. or Bias (X-m)
The acceptance criteria is mean value  15%
deviation from true value.
At LOQ, 20% deviation is acceptable.
Accuracy (%) = 100 x
Found value - Theoretical value
Theoretical value
Poor Precision
Good Accuracy
Good Precision
Poor Accuracy
Poor Precision
Poor Accuracy
Gold
Standard
Silver
Standard
Off-Base
Model
Hit or
Miss Model
Good Precision
Good Accuracy
Grap

1. Recovery test: Adding a known amount
of analyte to a base and measuring the
concentration
2. Specificity (cross-reactivity)
3. Interferences
4. Parallelism (Linearity)
Method
comparison
Comparing the results
to a reference values
obtained from a
definitive method,
How Accuracy Determined
Direct Indirect

31
Method Comparison
What do I do?
List results from two methods in pairs
- Each pair represents the same sample
X – results of reference method
Y – results of new method
1.
2. Create a scatter plot (plot the means of
in duplicate)
duplicates) if done
- May also use a difference plot to analyze data
Look for outliers and data gaps
- Repeat both methods for outliers
- Try to fill in gaps or eliminate highest data during analysis
3.

Linear Equations
Y
Y = bX + a
a = Y-intercept
X
Change
in Y
Change in X
b = Slope
bXayˆ 

Correlation
3. Parameters like m,
Y intercept, r, Bias, etc
2. Samples: At least 40 samples
(~200-300 serum samples)
1. Reference method
 Regression Statistics Review:
 Correlation Coefficient (r) - characterizes the
dispersion of results around the line of best fit.
 Slope - The “lean” of the line of best fit
(proportional bias)
 Y-Intercept - the point at which the line of best fit
intersects the Y axis. (constant bias)
 Acceptability Criteria:
 Correlation Coefficient (r) - the closer to 1.0 the
better
 Slope - The closer to 1.0 the better
 Y-Intercept - the closer to zero the better

Factors to consider Comparative method
Number of patient specimens
Single vs. duplicate measurements
Time Period
Specimen stability

34
Characteristics of r
“r” influenced by range of values
• r < 0.975 may indicate that the range of
limited
data is too
“r” is influenced by random errors only
Systematic error has no effect on r
• r is only used to assess linear relationship between methods
• Method accuracy should not be based on r

Indirect Accuracy Detection (Proportional
Error)
• Recovery (C-B/A)x100
Matrix Effect
Precision

 CLSI recommends four measurements of each
specimen; three are sufficient
 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)
Reportable Range / Linearity
 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)
Introduction
What is
needed
How we
perform the
testing
67

Reportable range
• Validation of Reportable Range Minimum of 3 test
specimens (4-5 better), measured in duplicate or triplicate
• Appropriate matrix
• Well established target concentrations
• Concentrations near the low, midpoint, and high values of
the AMR

Reportable Range:
How We Evaluate the Data
69
 Measured values on Y-axis versus
 Known or assigned values on X-axis
Plot mean values of:
Compare with expected values (typically
provided by manufacturer)
Visually inspect, draw best-fit line, estimate
reportable range

Reportable Range Activity
Assigned
Value
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
70

Reportable Range Activity (cont'd)
Assigned
Value
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
71

Reportable Range Activity (cont'd)
Linearity Scatter Plot
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
0 100 200 300 400 500 600 700 800 900
Assigned Concentrations (units)
RecoveredValues(Means)
72

Outliers
We can eliminate any point that differs from the next highest
value by more than 0.765 (p=0.05) times the spread
between the highest and lowest values (Dixon test).
Example: 4, 5, 6, 13
(13 - 4) x 0.765 = 6.89

Analytical Sensitivity
 Definition: Lowest reliable value; lower
limit of detection,
 Different terminologies used by different
manufacturers
Introduction
 Blank solutions
 Spiked samples
What is
needed
 20 replicate measurements over short or
long term, depending on focus
How we
perform the
testing
76

Limit of Detection
Limit of Blank (LoB):•
– The lowest concentration that can be distinguished from
background (blank, zero) noise
Sometimes called limit of absence.
Calculated as: Mean conc. of blank zero (>20 replicates) + 2SD
This is the number provided in most kit inserts
–
–
–
• Limit of Detection (LoD):
– The lowest number that will almost always have a non-zero
result (mean conc. of blank + 3 SD)
Limit of Quantification (LoQ):
–
–
The lowest concentration that can be quantified reliably
Analyte lowest concentration where CV 20% (or other
error goal)
Results with higher CV% have large random error, thus are not
useful for clinical interpretation
–

Analytical Sensitivity: How We Evaluate the Data
Lower Limit of
Detection (LLD):
Mean of the blank
sample, plus two or
three SD of blank
sample
Biological Limit of
Detection:
LLD plus two or
three times SD of
spiked sample with
concentration of
detection limit
Functional
Sensitivity:
Mean concentration
for spiked sample
whose CV = 20%;
lowest limit where
quantitative data is
reliable
78

Analytical Sensitivity
Definition: Smallest amount of analyte that can
be detected under the conditions of the assay
1. Lower limit of detection, ie., The least or minimum detection dose (LDD)
2. Minimum distinguishable difference in concentration, Resolution (MDDC)
The sensitivity of an analytical method is its
ability to give response to small changes in
the absolute amount of analyte present
1
2
3
High sensitivity
Concentration (X)
added quantity
Response (Y)
measured
quantity
Three analytical areas
1 2 3
Xb
not
detected
Area of
detection
Area of
quantification
or CV<20%
LOD LOQ

Good ELISA Practice, Check
Standard curve

Analytical Specificity
The ability of an analytical method to detect
“ONLY” the analyte of interest.
Freedom from interference by any element
or compound other than the analyte of
interest

Analytical Specificity
Analytical Specificity is verified using
interference studies.
-
- A validated method, known to be free of
the interfering substance is used. A series
of samples containing increased
concentrations of the interfering substance
are analyzed using that method, and the
method under study,
compared
then both results are

Analytical specific ity
See KnovmInterferingSubstances sectionfor
details.
Known lntertering Substances
Interference due to magnesium is negJigible at magnesium•
levels normally encountered in human serum. A maximum
positive interference of 0.7 mg/dL (0.17 mmol/L]d occurs at a
magnesium level of 7 mg/dL (2.9 rnrnol/Ll.?
• The tollowinq substances have no measurable effect on the CA
method at the
A c e t aminophen
� icil lin
Bilirubm
Diazepam
Digoxin
Ethanol
Gentamicin
Hemoglobin
Litllium heparin
Lipem_ia
Nortrtptyline
Phenobarbital
Phenytoi:n
Salicylate
S o d itun Flu o r id e
TileophyDine
concentration indicated:
2 0 0 µ g f m L
2 0 µ g f m L
2 0 m g / d L
20 µgldL
2 0 n g / m L
8 0 0 m g / d L
1 6 µgfm l.
5 0 0 m g / d L
2 8 0 U / m L
6 0 0 m g / d L
1000 ng/mL
8 0 µ g l m L
3 0 µgfm l.
100 mg/dL
4 0 0 m g / d L
1 0 0 µ g / m L
(1.3 mmoVLJ
[57 J.llTK)lfL)
(342 µmolJLJ
[70 J.lfTK>I/L]
[25_6 nrnol/L]
( 1 7 4 mmol /L]
(29_4 µmollL]
(0 _3 1 µ m o l/L (m o n o m e r)]
( 2 8 0 0 00 U/L]I?
(6_86 mJTlOlfL]t.riglyceride
(3797 nrnoUL]
[344 µmol! LJ
(11 9 µmol/L]
(7_24 mmolfLJ
[4 g/LJ�
[555 µmolJLJ
fppt.com

Indirect Accuracy Detection
• Interferences
Matrix effect
Proteins, RF,Complement,
Heterophilic Ab, Mechanical ,
Nonspecific, Hook effect

49
Interferences in Immunoassays
Non-specific binding
– High levels of immunoglobulins
– Immune complexes
Interfering antibodies
•
•
–
–
–
Rheumatoid factor
Specific antibodies to the analyte
Heterophile antibodies (antibodies
human proteins)
to reagent non-
• High concentrations of these types of substances
may be difficult to obtain. Interference studies
may require “mixing experiments”.

40
www.westgard.com
ReferenceInterval

Reference Intervals
91
 Definition: Normal range in healthy population
 Used for diagnosis/clinical interpretation of
results
Introduction
 Pre-defined “normal” criteria for screening
purposes
 Transferring: 20 “normal” individuals’ specimens
 Establishing: 120 “normal” individuals’ specimens
What is
needed
 Perform testing on all samples
 Document results
How we
perform the
testing

Reference intervals
Exclusion
age, sex,
/ partitioning criteria
disease
include:
history,fasting status,
drug history, previous surgeries, and time of
the cycle / pregnancy for females.

Transferring Establishing
 18 of 20 must
fall within
manufacturer’s ranges
 Calculate mean and SD
of data for each group
 Reference Intervals =
mean ± 2 SD (if Gaussian
Distribution only,
otherwise, additional
calculations
recommended)
93
Reference Intervals:
How We Evaluate the Data

Qualitative
Test
• Cut off
• Diagnostic Sensitivity
• Diagnostic Specificity
• Positive Predictive Value
• Negative Predictive Value
• Precision
• Accuracy
• Precision
• Reportable range
• Reference interval
• Analytical Sensitivity
• Analytical Specificity
• Interference
• Recovery
Quantitative
Test

Qualitative validation
1. Linearity with AMR and CRR – Not applicable for qualitative tests.
2. Analytical Sensitivity is the lowest concentration of an analyte that can be measured. For an FDA approved,
unmodified method, the manufacturer’s stated sensitivity (cut-off value) will be used.
3. Analytical Specificity is the determination of the effect of interfering substances. For an FDA approved,
unmodified method, the manufacturer’s stated specificity will be used.
4. Reference Ranges – Can be determined by the laboratory with laboratory director approval C5-C95 interval.
Verification of manufacturer’s stated reference range is not required.

An Overview of Qualitative Terms Related to Method Performance EP12 A2
Qualitative Concept
Precision
Repeatability (within run) 20 Pos . 20 Neg. control
Intermediate precision (long term)
Between run 1 Pos, 1 Neg once a day up to 5 replicate to reach 20 data
Reproducibility (interlaboratory)
C50
+20% C50
-20% C50
Accuracy
Closeness of agreement of a single measurement with “true value”
A minimum of 10 samples for each expected result. For example, if a test method gives
results of “Positive/Negative”, the accuracy study must include 10 known positives and 10
known negatives
Reference Specimens Panel

98
Diagnostic Tests Characteristics
 Clinical Sensitivity
 Clinical Specificity
 Predictive Value

Markerconcentration
- +Disease

A Test With Normally Distributed Values
Negative Positive
Degree of ‘positivity’ on test
%ofGroup
DISEASED
NON-DESEASED
Test cut-off
Assessing the performance
of the test assumes that
these two distributions
remain constant. However,
each of them will vary
(particularly through
spectrum or selection bias)

CASESNON-CASES
Performance of A Diagnostic Test
Negative Positive
Degree of ‘positivity’ on test
%ofGroup
DISEASED
NON-DESEASED
Test cut-off
FALSE
NEGATIVES
FALSE
POSITIVES

Natural Frequencies Tree
Population
100

In Every 100 People, 4 Will Have The Disease
Disease +
4
Disease -
96
Population
100
If these 100 people are representative of the population at
risk, the assessed rate of those with the disease (4%)
represents the PREVALENCE of the disease – it can also be
considered the PRE-TEST PROBABILITY of having the disease

OF THE 4 PEOPLE WITH THE DISEASE, THE TEST WILL DETECT 3
Disease +
4
Disease -
96
Test +
3
Test -
1
Population
100
In other words, the
sensitivity is 75%

AMONG THE 96 PEOPLE WITHOUT THE DISEASE, 7 WILL TEST POSITIVE
Disease +
4
Disease -
96
Test +
7
Test -
89
Test +
3
Test -
1
Population
100
In other words, the
specificity is 93%

POSITIVE
PREDICTIVE
VALUE = 30%
AMONG THOSE WHO TEST POSITIVE, 3 IN 10 WILL ACTUALLY HAVE THE
DISEASE
Disease +
4
Disease -
96
Test +
7
Test -
89
Test +
3
Test -
1
Population
100
This is also the
POST-TEST PROB-
ABILITY of having
the disease

NEGATIVE
PREDICTIVE
VALUE = 99%
AMONG THOSE WHO TEST NEGATIVE, 89 OF 90 WILL NOT HAVE THE
DISEASE
Disease +
4
Disease -
96
Test +
7
Test -
89
Test +
3
Test -
1
Population
100

CONVERSELY, IF SOMEONE TESTS NEGATIVE, THE CHANCE OF
HAVING THE DISEASE IS ONLY 1 IN 90
Disease +
4
Disease -
96
Test +
7
Test -
89
Test +
3
Test -
1
Population
100

PREDICTIVE VALUES AND CHANGING PREVALENCE
Disease +
4
Disease -
996
Population
1000
Prevalence reduced by an order
of magnitude from 4% to 0.4%

PREDICTIVE VALUE AND CHANGING PREVALENCE
Disease +
4
Disease -
996
Test +
70
Test -
926
Test +
3
Test -
1
Population
1000
Sensitivity and
Specificity
unchanged

POSITIVE
PREDICTIVE
VALUE = 4%
POSITIVE PREDICTIVE VALUE AT LOW PREVALENCE
Disease +
4
Disease -
996
Test +
70
Test -
926
Test +
3
Test -
1
Population
1000
Previously, PPV
was 30%

NEGATIVE
PREDICTIVE
VALUE >99%
NEGATIVE PREDICTIVE VALUE AT LOW PREVALENCE
Disease +
4
Disease -
996
Test +
70
Test -
926
Test +
3
Test -
1
Population
1000
Previously, NPV
was 99%

Qualitative validation
Method being Validated
Diagnostic Sensitivity and Specificity
(Results from Comparison Study) Total
Positive Negative
Positive # true positive (TP) # false positive (FP) TP+FP
Negative # false negative (FN) # true negative (TN) FN+TN
Total TP+FN FP+TN N
Calculate the estimated Diagnostic Sensitivity(True positive rate) = 100 x
[TP/(TP+FN)]
Calculate the estimated Diagnostic Specificity(True negative rate) = 100 x
[TN/(FP+TN)]
Calculate the percent Positive Agreement (Positive Predictive Value)
=100 x TP/(TP+FP)
Calculate the percent Negative Agreement (Negative Predictive Value)
=100 x TN/(TN+FN)
Compare the results calculated above with the manufacturer’s stated claims for
Sensitivity, Specificity and Agreement found in the test kit package insert.
Results must be equal to, or greater than, the manufacturer’s claims for the
method to be considered accurate.

Manufacturer claims in brochure (TSH)

Manufacturer claims in brochure (BHCG)

CLIA REGULATION 493.1253(2)
1. Precision
2. Accuracy
3. Reportable range
4. Reference interval
5. Analytical sensitivity
6. Analytical specificity
7. Other specifications Determine the assay performs in your
hands the way the manufacturer says it performs

PRECISION STUDIES: WITHIN-RUN
PRECISION
– Patient or QC samples assayed 20 times on the same day within the
same run
– If precision poor, no need to do further eval
• Samples at least 2 - 3 levels – medical decision points

BETWEEN-RUN PRECISION
– Patient or QC samples once per day for 5-20 days
– Establish qc range as well as total precision
• Samples at least 2 - 3 levels – medical decision points

ACCURACY –CORRELATION
• Comparison of Methods – correlation Select a minimum of 20
(usually 40 – 60) patient’s serum samples with analyte values
as evenly distributed throughout the linear reportable range
of the assay as possible
• Assay all samples by the current method (comparative or
reference method – x-axis data) and the method being
evaluated (test method – y-axis data)

Overview of the protocol
• Precision evaluation experiment
– Repeatability, Reproducibility
• Trueness evaluation experiment
– Comparability (20 serum samples)
– Recovery of expected values from certified reference materials

Precision
Experiment
Example
Repeated measurements over 5
days of at least
two patients’ samples (three
replicates per run)
• basic design (3×5)/sample
Specimens used:
• Patient samples
• Pool of patient samples
• Commercially available
quality controls

Trueness Verification
• Correlation Study (Excel)

Find out the problems
in previous slide film?

Bias Estimation
No. Test Reference
1 6 7.94
2 5.97 7.94
3 8.7 11.4
4 9.3 11.9
5 6.6 7.8
6 7.1 8.2
7 9.68 11.6
8 6 7.5
9 7.39 8.7
10 6.5 7.6
11 8.4 9.7
12 7 8.2
13 7 8
14 7.88 9
15 7.4 8.6
16 5 6.2
17 9.93 10
18 10 11
19 7.1 8
20 10 9.8
Mean 7.6475 8.954
Bias% -14.6%

Practical Example
ELISA Kit Verification
Reference :
The Immunoassay Handbook
Theory and applications of ligand binding,
ELISA and related techniques
Edited by
David Wild

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Method Evaluation
Description (2 Kits) Replication
Non-specific binding (NSB) or no sample 2
Set of controls normally used by laboratory 4 x 2
Zero calibrator 10
Remaining Calibrators 5 X 2
Kit manufacturer’s controls 3 x 2
Patient samples 12 x 2
Sample dilutions (1/2, 1/5, 1/10) 3 x 2
Diluent 1 x 2
External QC scheme samples 5 X 2
Set of controls normally used by laboratory 5 x 2

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Method Evaluation
Analysis of results from initial kit
evaluation
 The calibration curve should be fitted as
recommended by the manufacturer
 Within assay precision
%CV for the controls, samples and calibrators
(Value not OD)
 Between-assay differences and stored calibration
curve stability
% CV for control and sample
Compare the values generated by the stored
calibration curve with those derived from a manual
plot of all the calibrators

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Method Evaluation
evaluation (continue)
 Drift
Plot the values of controls obtained at the
beginning, middle and end of the assay to detect
assay drift
 Sensitivity
10 replicates of zero calibrator, Analytical
sensitivity is two SD above or below the zero
calibrator mean
 Accuracy
Compare the results for the external QC scheme samples
with those obtained from other methods and all-laboratory
trimmed means
Compare the patient samples with current method

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Method Evaluation
evaluation (continue 2)
 Dilution
Samples diluted by zero calibrator, Plot the dilution
curve, straight line
 Verification of Reference Interval
 Other information
1. Check the appearance of the reagents,
2. Check the ease of using of packaging
3. Quality of the instructions
4. Estimate the total assay time
5. Telephone to customer service and ask one/two
questions to check the quality and the speed of
their responses
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Last Phase in Both
Validation and
Verification?????

137
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Validation and verification of immunoassay methods dr. ali mirjalili

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