Download ATTACHMeNT 1: Instructions to assist NPPOs and RPPOs in proper

International Plant Protection Convention
04_TPPT_2016_Jul
Examples of estimated numbers of treated pests using both direct and modified methods Agenda item: 4.2
ESTIMATING TREATED NUMBERS FROM CONTROL EMERGENCE
(Prepared by Mr Mike Ormsby)
BACKGROUND:
[1]
This paper was last discussed at the Technical Panel on Phytosanitary Treatments (TPPT) in March 2016
virtual meeting1. The TPPT pointed out that more discussion and examples should be given on how the
use of this formula could affect the estimated number.
The TPPT is invited to:
(1)
(2)
(3)
Review this document and provide comments;
Agree with the proposed formula;
Consequently, if agreement is reached, revise the “Instructions to assist NPPOs and RPPOs to assist
in proper and complete treatment submissions” section 4 on “General considerations when
calculating the level of efficacy achieved by a treatment schedule”. The proposed text is presented
as attachment 1 of this document (black text)2.
DISCUSSION:
[2]
The statistical issue associated with estimating treated or exposed populations from control numbers was
recognised as long ago as 19493, in that “When the number of organisms treated in a dosage-mortality
study is not known exactly, but is estimated from a sample, weighting must be modified.”
[3]
When the population of treated pests is estimated from control pest populations, the estimation must be
based on a statistical analysis of the controls. Where possible, control data should not be grouped together,
but should be recorded for each individual test commodity or target pest.
[4]
Researchers need to apply the same statistical rigour to control data as they do to treatment data. Where
the infestation rate for each regulated article in the control is known, the estimated treated regulated article
infestation rate would be:
[5]
Average per treated regulated article = µ - (STD × 1.645)
[6]
Where the control infestation rate is based on the mean of grouped commodities, as the number of controls
increases so does the level of confidence in the estimation of the population mean. A suitable formula for
estimating the average number of exposed pests per treated regulated article would therefore be:
[7]
Average per treated regulated article = µ - (STD × (1+1/r))
[8]
Note: r is equal to the number of control replicates used to estimate the mean (µ) and standard deviation
(STD) of the control means.
[9]
The formula for determining the average per treated regulated article based on the mean of grouped
control commodities has been derived as follows:
-
Let Ti be the transformed counts for the treated portions and Cj be the transformed counts for the
control portions and let Ta and Ca be the averages of these.
1
TPPT March 2016 virtual meeting report:
https://www.ippc.int/static/media/files/publication/en/2016/05/Report_TPPT_2016_Mar_2016-05-03.pdf
2
Please refer to document 03_TPPT_2016_Mar of the TPPT March 2016 virtual meeting and to the meeting report.
3
Wadley F. M. (1949) Dosage-mortality cor.elation with number treated estimated from a parallel sample. Annals
of Applied Biology, 36: 196–202.
International Plant Protection Convention
Page 1 of 5
Examples of estimated numbers of treated pests using both direct and modified methods 04_TPPT_2016_Jul (4.2)
-
We need an estimate of the common variance so let s2 be the pooled variance of the transformed
counts about their respective means.
The common variance (s2) is obtained from an analysis of variance of the transformed counts
𝟏
𝟏
or 𝐬𝟐 × (𝐦 + 𝐫 ) where m equals the number of treated samples (T) and r equals the number of control
samples (C).
-
The standard error4 of this is equal to 𝐒𝐄 × √(𝐦 + 𝐫 ).
-
In the case where there is just 1 treated sample (m=1), we get a standard error of
-
𝟏
𝟏
𝟏
𝐒𝐄 × √(𝟏 + )
𝐫
-
The average per treated regulated article can therefore be estimated as the mean of the control
𝟏
̅) ± (𝐒𝐄 × √(𝟏 + )).
samples (𝒙
𝐫
-
As we want the most conservative estimate (to have 95% confidence that we do not have any false
𝟏
̅) − ( 𝐒𝐄 × √(𝟏 + )).
negatives) we use the estimate (𝒙
𝐫
[10]
The following are three examples of published treatment research that estimated treated numbers from
control emergence. The examples provide a comparison of the estimated treated numbers using direct
calculations from control data verses using the statistical adjustment described above.
[11]
Example 1: Santaballa E., Laborda R., Cerdá M. (2009) Quarantine cold treatment against Ceratitis
capitata (Wiedemann) (Diptera: Tephritidae) to export clementine mandarins to Japan. Bol. San. Veg.
Plagas 35: 501-512
Mandarins
2°C/16 days
Confirmatory Controls - MedFly
TREATMENT
UNIT (r)
No.
FRUIT /
TRAY
No. Pupae
1st Count
TOTAL #
PUPAE
AVERAGE /
FRUIT
1
294
4527
4527
15.40
2
286
4118
4118
14.40
3
302
4167
4167
13.80
Total
882
12,812
12,812
14.53
14.53 ± 0.93 =
13.60
0
0
Mean (± SE x (SQR(1+1/r)) =
Number Tested Fruit =
2,202
Estimated Number of Treated FF (95% confidence) =
29,940
Estimated Number of Treated FF (unadjusted) =
31,995
4
McBride G., Cole R G., Westbrooke I. & Jowett I. (2014) Assessing environmentally significant effects: a better
strength-of-evidence than a single P value? Environ Monit Assess 186: 2729–2740
Page 2 of 5
International Plant Protection Convention
04_TPPT_2016_Jul (4.2) Examples of estimated numbers of treated pests using both direct and modified methods
[12]
Example 2: De Lima, C.P.F., Jessup, A.J., Cruickshank, L., Walsh, C.J. & Mansfield, E.R. (2007) Cold
disinfestation of citrus (Citrus spp.) for Mediterranean fruit fly (Ceratitis capitata) and Queensland fruit
fly (Bactrocera tryoni) (Diptera: Tephritidae). New Zealand Journal of Crop and Horticultural Science,
35: 39–50.
Valencia orange
2°C
Confirmatory Controls
No. Pupae
TREATMENT
UNIT (r)
No.
FRUIT /
TRAY
1st sieve
2/5/00
1
1200
2
TOTAL # PUPAE
AVERAGE/
FRUIT
45,918
45,918
38.27
1200
47,724
47,724
39.77
3
1200
47,799
47,799
39.83
Total
3600
141,441
141,441
39.3
39.3 ± 1.0 =
38.3
2nd sieve
9/5/00
0
Mean (± SE * SQR(1+1/r)) =
Number Tested Fruit =
Valencia orange
Estimated Number of Treated FF (95% confidence) =
137,752
Estimated Number of Treated FF (unadjusted) =
141,441
3°C
Confirmatory Controls
No. Pupae
TREATMENT
UNIT (r)
No.
FRUIT /
TRAY
1st sieve
2/5/00
1
1200
2
TOTAL # PUPAE
AVERAGE/
FRUIT
50,283
50,283
41.90
1200
44,943
44,943
37.45
3
1200
47,358
47,358
39.47
Total
3600
142,584
142,584
39.6
39.6 ± 2.6 =
37.0
2nd sieve
9/5/00
0
Mean (± SE * SQR(1+1/r)) =
Number Tested Fruit =
Naval orange
3,600
Estimated Number of Treated FF (95% confidence) =
133,321
Estimated Number of Treated FF (unadjusted) =
142,584
2°C
Confirmatory Controls
No. Pupae
TREATMENT
UNIT (r)
No.
FRUIT /
TRAY
1st sieve
2/5/00
1
1200
2
TOTAL # PUPAE
AVERAGE/
FRUIT
54,012
54,012
45.01
1200
54,327
54,327
45.27
3
1200
57,555
57,555
47.96
Total
3600
165,894
165,894
46.1
46.1 ± 1.9 =
44.2
2nd sieve
9/5/00
0
Mean (± SE * SQR(1+1/r)) =
Number Tested Fruit =
[13]
3,600
3,600
Estimated Number of Treated FF (95% confidence) =
159,101
Estimated Number of Treated FF (unadjusted) =
165,894
Example 3: Summer-fruit data (Australian report)
Rep1
1°C
International Plant Protection Convention
Confirmatory Controls - Qfly
Page 3 of 5
Examples of estimated numbers of treated pests using both direct and modified methods 04_TPPT_2016_Jul (4.2)
No. Pupae
TREATMENT
UNIT
No.
FRUIT /
TRAY
1st sieve
09/04/07
2nd sieve
16/04/07
3rd sieve
23/04/07
1
15
1279
5
2
15
570
3
15
4
Totals
TOTAL #
PUPAE
AVERAGE /
FRUIT
0
1284
85.60
41
0
611
40.73
1519
86
0
1605
107.00
15
1670
143
0
1813
120.87
60
5,038
275
0
5,313
88.55
39.16 =
49.39
Average (± SE x (SQR(1+1/r)) =
88.55 ±
Number Tested Fruit =
Rep2
1°C
TREATMENT
UNIT
No.FRUIT
/ TRAY
1
274
Estimated Number of Treated FF (95% confidence) =
13,534
Estimated Number of Treated FF (unadjusted) =
24,262
Confirmatory Controls - Qfly
No. Pupae
TOTAL #
PUPAE
AVERAGE /
FRUIT
0
1146
114.60
132
0
1007
100.70
945
128
0
1073
107.30
10
776
114
0
890
89.00
5
10
954
346
0
1300
130.00
6
10
1198
103
0
1301
130.10
Total
60
5681
1036
0
6717
111.95
17.66 =
94.29
1st sieve
23/04/07
2nd sieve
30/04/07
3rd sieve
07/05/07
10
933
213
2
10
875
3
10
4
Average (± SE x (SQR(1+1/r)) =
111.95 ±
Number Tested Fruit =
Combined Rep1 and Rep2
Estimated Number of Treated FF (95% confidence) =
28,286
Estimated Number of Treated FF (unadjusted) =
33,585
Confirmatory Controls – Qfly (1°C)
Total Number of Tested Fruit =
Page 4 of 5
300
574
Total Estimated Number of Treated FF (95% confidence) =
41,820
Total Estimated Number of Treated FF (unadjusted) =
57,847
International Plant Protection Convention
ATTACHMENT 1
04_TPPT_2016_Jul (4.2)
ATTACHMENT 1: INSTRUCTIONS TO ASSIST NPPOS AND RPPOS IN PROPER
AND COMPLETE TREATMENT SUBMISSIONS
[14]
4.
General considerations when calculating the level of efficacy achieved by a treatment schedule
The panel has recommended a number of principles that should be apply when calculating the efficacy
achieved by a treatment schedule at the 95% confidence level, based on the total number of target pests
treated. Further information on the calculation of the level of efficacy is provided in a publication by
Couey and Chew (1986). These principles include:
-
Percentage mortality of treated target pests should be adjusted for mortality in the control by the
following formula: Ya = 100% - [(X – Y)/X](100%), where Ya is the adjusted percentage
surviving in the treated cohort, X is the percentage surviving in the control and Y is the percentage
surviving in the treated cohort (Abbott 1925).
-
Greater than expected response levels in controls may indicate a target pest population under
stress that may be more susceptible to the treatment than a natural population. If control response
is high, evidence should be provided that either indicates pest susceptibility to the treatment is no
greater than normal populations or that high control response reflects normal conditions;
-
Sample sizes and repetitions should be sufficient to account both for natural variation and achieve
significant regressions when extrapolating treatment efficacy. A small number of treatment
repetitions can, on analysis, result in statistical errors giving meaningless conclusions (if the SD
at 95% is greater than the mean, the lower (worst case) result may be a negative dose e.g. 10 ±
12 gives a range from -2 to 22).
-
When the population of treated pests is estimated from control pest populations, the estimation
must be based on a statistical analysis of the controls, control data should not be grouped together,
but should be recorded for each individual test commodity or target pest. Pseudo-replication5
should be avoided or minimized, as much as possible;
-
Where the infestation rate for each regulated article in the control is known but the infestation
rate in treated articles is unknown , the estimated treated article infestation rate would be:
Average per treated regulated article = μ - (STD × 1.645)
-
Where the control infestation rate is based on the mean of grouped commodities, as the number
of controls increases so does the level of confidence in the estimation of the population mean. A
suitable formula for estimating the average number of exposed pests per treated regulated article
would therefore be:
̅ − (SE × √𝟏 + (𝟏 ÷ 𝒓))
Average per treated regulated article = 𝒙
̅) of the controls
Note: r is equal to the number of control replicates used to estimate the mean (𝒙
and the standard error (SE) of the mean.
5
Pseudo-replication is used to test for treatment effects with data from experiments where either treatments are not replicated
(though samples may be) or replicates are not statistically independent. The error described by this term arises when treatments
are assigned to units that are sub-sampled and the treatment F-ratio in an analysis of variance (ANOVA) table is formed with
respect to the residual mean square rather than with respect to the among unit mean square. The F-ratio relative to the within unit
mean square is vulnerable to the confounding of treatment and unit effects, especially when unit number is small (e.g. four tank
units, two tanks treated, two not treated, several subsamples per tank). The error is avoided by forming the F-ratio relative to the
among unit mean square in the ANOVA table (tank MS in the example above). Pseudo-replication, as originally defined, is a
special case of inadequate specification of random factors where both random and fixed factors are present:
http://en.wikipedia.org/wiki/Pseudoreplication
International Plant Protection Convention
Page 5 of 5