Transcript Talk

ISQC2013
A note on precision
of qualitative data
Tomomichi Suzuki, Tokyo University of Science
[email protected]
Yusuke Tsutsumi, Mitsubishi Tanabe Pharma Corporation
Natsuki Sano, Tokyo University of Science
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Introduction of myself
• I focus on “Statistical Data Analysis” that will bridge the gap between
theory and practice.
• I am attending ISQC for the fourth time
• Warsaw 2004, “Effective Dynamic Process Control of Assembly
Processes”
– statistical control of assembly process with time dependent noise
• Beijing 2007, “A Study on Adaptive Paired Comparison
Experiments”
– design of experiment for paired comparison
– proposal on the Swiss tournament system
• Seattle 2010, “Improving Taguchi’s linear graphs for split-plot
experiments”
– proposed new linear graphs for expressing interaction of whole-plots
and sub-plots
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Introduction
• ISO 5725 accuracy (trueness and
precision) of measurement methods and
results
• Tests performed on presumably identical
materials in presumably identical
circumstances do not, in general, yield
identical results.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Introduction
• ISO 5725 accuracy (trueness and precision)
of measurement methods and results
• Trueness:
– refers to the closeness of agreement between
the arithmetic mean of a large number of test
results and the true or accepted reference value.
• Precision:
– refers to the closeness of agreement between
test results.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
ISO/TC 69/SC 6
• ISO/TC 69 (Application of Statistical
Methods)/SC 6 (Measurement Methods and
Results)/WG1 (Accuracy of measurement
methods and results) is preparing a document
(TR: Technical Report) on precision of
qualitative data. Now in Preliminary Work Item.
• Reviewed existing methods and established
methods.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
ISO/TC 69/SC 7
• ISO/TC 69 (Application of Statistical
Methods)/SC 7 (Six Sigma) published ISO TR
14468 “Selected illustrations of attribute
agreement analysis”
• This is based on kappa coefficient approach.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
ISO/TC 34/SC 9
• ISO/TC 34 (Food products) /SC 9 (Microbiology)
produced ISO 16140 “Microbiology of food and
animal feeding stuffs — Protocol for the validation
of alternative methods” in 2003.
• It includes method by Langton et al. (2002)
• ISO/TC 34/SC 9 is revising ISO 16140
“Microbiology of food and animal feed — Method
validation — Part 2: Protocol for the validation of
alternative (proprietary) methods against a
reference method”.
• It includes method by Wilrich.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
AOAC International
ISO/TC 34/SC 16
• ISPAM (International Stakeholder Panel on Alternative
Methods) produced a document on “Guidelines for
Validation of Qualitative Chemistry Methods” which is
based on POD model proposed by P. Wehling et al.
(2011)
• This is the main part of the ISO/TC 34 (Food products)
/SC 16 (Horizontal methods for molecular biomarker
analysis) document. “Validation Scheme for
Qualitative Analytical Methods” (possible alternative
title: "Performance characteristics and validation of
binary measurement methods")
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
IMEKO/TC 21
• IMEKO (International Measurement
Confederation) / TC 21 (Mathematical
Tools for Measurements) hold SIG
(Special Interest Group) “Precision
evaluation in non-quantitative
measurements”.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Purpose
• Many methods are proposed for qualitative
data, but their effectiveness and statistical
properties are not so clear.
• This paper introduces the methods to
evaluate precision for qualitative data,
then proposes a method using logit model.
The proposed method is compared with
existing methods.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Collaborative Assessment
Experiment
• Every laboratory measures the identical test
item number of times.
Laboratory
Run 1
...
Run k
...
Run n
1
y11
y1k
y1n
2
y21
y2k
y2n
yi1
yik
yin
yL1
yLk
yLn
:
:
i
:
:
L
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Quantitative Data
• Repeatability:
– is the precision under repeatability conditions
– where independent test results are obtained with the same
method on identical test items in the same laboratory by
the same operator using the same equipment within short
intervals of time.
– Repeatability indicates the smallest variation for a
particular measurement method.
• Reproducibility:
– is the precision under reproducibility conditions
– where test results are obtained with the same method on
identical test items in different laboratories with different
operators using different equipment.
– Reproducibility indicates the largest variation for a
particular measurement method.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Quantitative Data
• Model used in ISO 5725
y=m+B+e
– y is the measurement result
– m is the general mean (expectation)
– B is the laboratory component of bias under
repeatability conditions (variance sL2)
– e is the random error in every measurement
under repeatability conditions. (variance se2)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Quantitative Data
• Repeatability variance sr2
sr2 = se2 , or
s r  V (e)
• Reproducibility variance sR2
sR2 = sL2 + sr2 = sL2 + se2 , or s R  V ( B)  V (e)
(1)
• The estimates of repeatability variance and
reproducibility variance are calculated from
interlaboratory studies or collaborative assessment
experiments.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Quantitative Data
• Gauge R & R
• Many objects are measured (there are variation in products)
• Gauge Repeatability = Repeatability in ISO 5725 (sr2)
• Gauge Reproducibility ≠ Reproducibility in ISO 5725
• Gauge Reproducibility = Between Laboratory Variance
in ISO 5725 (sL2)
sR2 = sL2 + sr2
(1)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Qualitative Data
• Non-quantitative measurements
– binary data, categorical data, ordinal data, etc.
• In this paper, the methods to evaluate precision for
binary data are considered.
• Methods compared are
–
–
–
–
Accordance and concordance (Langton’s)
Attribute agreement analysis (Kappa)
van Wieringen’s method
Proposed method
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Precision for Quality Data
• The value of yik is either 0 (negative, non-detect,
fail, etc.) or 1 (positive, detect, pass, etc.).
Table 1
Laboratory
Run 1
...
Run k
...
Run n
1
y11
y1k
y1n
2
y21
y2k
y2n
yi1
yik
yin
yL1
yLk
yLn
:
:
i
:
:
L
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
0. ISO Based Method
yij     i     eij
Wilrich’s model
y  m Be
ISO5725 model
Repeatability Variance
 n  1 L
sˆ  
   ˆi 1  ˆi 
 n  1  L i 1
2
r
Inter-laboratory Variance Component

：general mean
eij
：laboratory
component of bias
：random error
 i   
ˆi ：Estimate of probability of
detecting for lab i (i=1, 2, …, L)
n ：number of repetitions
(measurements)


1 L
 1  1 L
2
ˆ
ˆ
ˆ
ˆ




sˆ  max 0,






1



  i

i
i 

 n  1  L i 1
 L  1 i 1

2
L
Reproducibility Variance
sˆ R2  sˆ r2  sˆ L2
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
1. Accordance and Concordance
Lab i
１
Accordance
１
１
xi ( xi  1)  (n  xi )(n  xi  1)
Ai 
n(n  1)
１
０
matching
probability Ai
1 L
A   Ai
L i 1
Ai ：Accordance for laboratory i (i1, 2, ..., L)
A ：Accordance
xi ：number of ‘detect’ for lab i (i1, 2, ..., L)
n ：number of repetitions (measurements)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
1. Accordance and Concordance
lab i’’
lab i
１
Concordance
１
１
１
matching
probability Ci
０
 L

2 xi   xi  nL  nLnL  1  Ai nL(n  1)

Ci  i 1  i 1
n 2 L( L  1)
lab i’
１
１
１
１
１
０
０
１
１
１
L
1 L
C   Ci
L i 1
Ai ：Accordance for laboratory i (i1, 2, ..., L)
Ci ：Concordance for laboratory i (i1, 2, ..., L)
C ：Concordance
xi ：number of ‘detect’ for lab i (i1, 2, ..., L)
n ：number of repetitions (measurements)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
1. Accordance and Concordance
• Relation between ISO based method and
accordance, concordance
1 A
 sˆ r2:ISO 5725 based
2
A : Accordance
,
1 C
 sˆ R2:ISO 5725 based
2
C : Concordanc e
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
2. Van Wieringen et al(2008)
X ijh ：measurement
・binary 1: detect, pass
0: no-detect, fail
model
Yi
PX i ,1,1 , X i ,1, 2 ,..., X i ,m,l Yi    PX ijh Yi 
j ,h
：true value
i  1,...,n　j  1,...,m　h  1,...,l
items
appraisers
repetitions
true probability of ‘pass’   P(Yi  1)
Sensitivity
 j 1
Specificity
1   j 0

where  j  y   P X ijh  1Yi  y
latent class model

PX ijh  x   1    j (0) x 1   j (0)   j (1) x 1   j (1)
1 x


1 x
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
2. Van Wieringen et al(2008)
Likelihood when
   , 1 1,..., m 1, 1 0,..., m 0
T
m
m

 l 
 l 
l R
R
l  Rij
Rij

LR;     1     1   j 0  j 0      1   j 1 ij  j 1 ij

i 1 
j 1  Rij 
j 1  Rij 

n
 





where Rij  h 1 X ijh
l
Maximum Likelihood Estimate using EM algorithm
Maximum Value: LRR
1 (0)    m (0) and 1 (1)     m (1)
n 
 m l
ml  Ri
Ri  m l 
ml  Ri
Ri


 j 1   
LR;      1    1   j 0
 j 0   1   j 1
i 1 
 Ri 
 Ri  
Likelihood when

Maximum Likelihood Estimate using EM algorithm



Maximum Value: LR
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
2. Van Wieringen et al(2008)
Repeatability
Reproducibility
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
3. Attribute Agreement Analysis
Fleiss’ Kappa Statistic
Pˆo  Pˆe
ˆ 
1  Pˆe
n M


1
2
ˆ


Po 
x

nm

ij

nm(m  1)  i 1 j 1

M
Pˆe   p 2j
j 1
Pˆo ：Probability that results actually matched n ：number of items
Pˆe ：Probability that results match by chance m ：number of appraisers
p j ：Ratio of category j
1    1
M ：number of categories
xij ：number of
item i categorized as j
  1 complete agreement
  0 the same as chance (no correlation)
  1 complete non-agreement
 within appraisers
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 between appraisers
2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
4. Proposed Method
• We propose the method of estimating repeatability
and reproducibility using the logit transformation.
• When the number of positive results xi follows a
binomial distribution with parameters n and pi,
then logit transformation of pi* asymptotically
follows a normal distribution as follows.


i
1


Li ~ N  ln
,
 1   i n i 1   i  
where
Li  Logit i *  ln
i *
1 i *
(4)
and  i * 
x i  0 .5
n 1
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Qualitative Methods
4. Proposed Method
• When we consider xi as the measurement result of
laboratory i, the variances can be estimated by
means of one-way ANOVA as shown below.
• Repeatability Variance
1 L
1
ˆ
s  
L i 1 nˆi 1  ˆi 
2
r
(6)
• Reproducibility Variance
2
L
L




1
2
2

sˆ R 
Li    Li  L 


L  1  i 1
 i 1 

(7)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Comparison of methods
• Methods are compared using the same set of data
in order to clarify the relation of among the
methods.
–
–
–
–
Accordance and concordance,
Attribute agreement analysis (Kappa)
van Wieringen’s method
Proposed method
• We compared the methods by averaging the
obtained precision measures in the case for
Langton's method and the proposed method.
• The parameters are set based on van Wieringen's
method.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Comparison of methods
• Values of parameters (of figures next page)
– overall  (probability of an item being
conforming): 0.5
– number of items: 200
– number of raters L: 3
– number of repetitions for each rater n: 3
– probability of evaluating conforming item as pass:
0.99, 0.95, 0.90
– probability of evaluating nonconforming item as
pass: 0.01, 0.05, 0.10
(those probabilities are for raters 1 to 3
respectively)
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Comparison of methods
Results
• Repeatability(above) and Reproducibility(below)
1200
1150
1100
1050
1000
0.76
11.0
0.75
10.9
0.74
10.8
0.73
10.7
logit(r)
1250
κ within raters
van Wieringen(r)
1300
0.72
0.71
0.70
0.84
0.85
0.86
0.87
0.88
10.4
10.3
0.68
10.2
0.89
10.1
0.83
0.84
0.85
Accordance
0.86
0.87
0.88
0.89
0.83
0.84
0.85
Accordance
140
0.78
120
0.76
0.86
0.87
0.88
0.89
Accordance
7.45
7.40
100
80
60
40
20
7.35
7.30
0.74
logit(R)
κ between raters
van Wieringen(R)
10.5
0.69
0.67
0.83
10.6
0.72
0.70
7.25
7.20
7.15
7.10
7.05
0.68
7.00
0
0.66
0.82
0.83
0.84
0.85
0.86
Concordance
0.87
0.88
0.89
6.95
0.82
0.83
0.84
0.85
0.86
Concordance
0.87
0.88
0.89
0.82
0.83
0.84
0.85
0.86
0.87
0.88
0.89
Concordance
• Strong relation among the methods. But not identical.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Comparison of methods
Results
• Accordance and concordance, attribute
agreement analysis and the proposed
method gave very similar results.
• The method proposed by van Wieringen
also gave similar results but the
relationship was not as strong.
• The reason for giving different precision
measures should be investigated.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Outline of Today’s Talk
•
•
•
•
•
Introduction
Precision for Quantitative Data
Precision for Qualitative Data
Comparison
Conclusions
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
Conclusions
• The method utilizing logit transformation is proposed.
• The proposed method and other existing methods are
compared using the same set of data.
• Accordance and concordance, attribute agreement
analysis and the proposed method gave very similar
results.
• The method proposed by van Wieringen also gave
similar results but the relationship was not as strong.
• Other methods (POD models etc.) should also be
compared. How to compare is the problem.
• It would be expected that these findings contribute to
standardization of evaluating precision of binary
measurements.
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
References
• Danila O., Steiner, S. H., and Mackay R. J. (2008). Assessing a
Binary Measurement System. Journal of Quality Technology, 40,
310-318.
• Fleiss, J. L. (1981). Statistical Methods for Rates and Proportions.
2nd edition, John Wiley & Sons.
• Horie K., Tsutsumi Y., Suzuki T. (2008). Calculation of Repeatability
and Reproducibility for Qualitative Data. Proc. 6th ANQ Congress,
12 pages (CDROM).
• ISO 5725 (1994). Accuracy (trueness and precision) of
measurement methods and result – Part 1 : General principles and
definitions.
• ISO 5725 (1994). Accuracy (trueness and precision) of
measurement methods and result – Part 2 : Basic methods for the
determination of repeatability and reproducibility of a standard
measurement methods.
• ISO/TR 14468 (2010). Selected illustrations of attribute agreement
analysis.
• Langton, S.D., Chevennement, R., Nagelkerke N., and Lombard B.
(2002). Analysing collaborative trials for qualitative microbiological
methods: accordance and concordance. International Journal of 39 /41
Food Microbiology, 79, 175-181.
2013-08-22 Tomomichi Suzuki, Tokyo University of Science
ISQC2013
References
• Mandel, J. (1997). Repeatability and Reproducibility for Pass/Fail
Data. Journal of Testing and Evaluation, 25, 151-153.
• Van der Voet, H. and van Raamsdonk L. W. D. (2004). Estimation of
accordance and concordance in inter-laboratory trials of analytical
methods with qualitative results. International Journal of Food
Microbiology, 95, 231-234.
• Wehling, P., LaBudde, R.A., Brunelle, S. L., and Nelson, M. T.
(2011). Probability of Detection (POD) as a statistical model for the
validation of qualitative methods. Journal of AOAC International, 94,
335-347.
• Van Wieringen, W. N., and de Mast, J. (2008). Measurement
System Analysis for Binary Data. Technometrics, 50, 468-478.
• Wilrich, P.-Th. (2010). The determination of precision of qualitative
measurement methods by interlaboratory experiments. Accreditation
and Quality Assurance, 15, 439-444.
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ISQC2013
Thank you for your attention!
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2013-08-22 Tomomichi Suzuki, Tokyo University of Science