Calibration is mandatory .... why?

1. Computer Simulation in
Medical Diagnostic Assay
Calibration Evaluation
Ping Shi Ph.D
Abbott Laboratories
Sept. 16, 2005

2. 2
Contents
 Background of Calibration
 Calibration Evaluation
 Computer Simulation
 Comments

3. 3
What is Calibration?
 Definition (ANSI/NCSL,1994)
The set of operations which establish,
under specified conditions, the
relationship between values indicated
by a measuring instrument or
measuring system, and the
corresponding standard or known
values derived from the standard.

4. 4
Calibration Process
 Reference standard with known values
(calibrators) to cover the range of
interest.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

5. 5
Calibration Process
 Measurements on the calibrators with
the instrument/reagent lot to be
calibrated.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

6. 6
Calibration Process
 Mathematical function relationship
between the measured responses and
the known values of the reference
standards, called a Calibration
Curve.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

7. 7
Calibration Process
 Translation of the measurements of
samples to the values of interest by
the inverse of the calibration curve.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

8. 8
Calibration Curves
Quatitative Assay Calibration Curve
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate
QualitativeAssayCalibrationCurve
0
2000
4000
6000
8000
10000
0 0.5 1 1.5 2
S/CO
Rate

9. 9
Calibration Determination
 Calibrator values
 Model and weight in calibration curve
fitting
 Number of replicate at each calibrator
level
 Stored calibration curve use

10. 10
Evaluation of Calibration
 Accuracy - Bias
 Precision - Variance Components (of
calibration, and more important, of
instrument, reagent lot, run, etc.)
 Sensitivity
 Specificity

11. 11
Problems
 Very limited calibration curves available
due to:
 Instrument capacity
 Testing material limitation
 Time limitation
 Manpower/Laboratory availability

12. 12
Simulation Approach
 Build mean and SD profiles
 Simulate calibration curves based on
the profiles
 Evaluate and select calibration methods

13. 13
Mean and SD Profiles
 Run multiple replicates at each of
several samples with known values
across assay dynamical region.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

14. 14
Mean and SD Profiles
 Build mean and SD profiles of response
variable over the region based on the
test results.
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Mean
0
10
20
30
40
50
0 20 40 60 80 100 120
Conc
SD

15. 15
Calibration Curve Simulation
 Simulate a sample mean at each of six
calibrator concentration levels based on
the profiles
 sample_mean=
mean+SD/sqrt(n)*random(seed)

16. 16
Calibration Curve Simulation
 Fit the calibration curve
 Replicate the process to get multiple
simulated calibration curves
0
1000
2000
3000
4000
0 20 40 60 80 100 120
Conc
Rate

17. 17
Calibration Method Evaluation
 Evaluate bias, precision, sensitivity and
specificity for each calibration method.
1500
2500
3500
10 20 30 40 50
Conc
Rate
0
4
8
12
25 30 35 40 45
Conc
Frequency

18. 18
Comments
 The same approach applies to other
calibration methods such as the ones
used in qualitative assays.
 When evaluating calibration curves,
different criteria might be used in
different region due to clinical
importance.