UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

MEMORANDUM 

Date: 8/21/14 

OFFICE OF 

CHEMICAL SAFETY AND 

POLLUTION PREVENTION 

SUBJECT: Metrafenone. Review of Residue Data from Hops Field Trials- Statistical Test 

for Outlier. 

PC Code: 000325 

Decision No.: 484338 

Petition No.: 3E8211 

DP Barcode: D421923 

Registration No.: 7969-284 

Regulatory Action: Section 3 Registration 

Assessment Type: Single Chemical Case No.: NA 

Aggregate 

TXRNo.: NA CAS No.: 220899-03-6 

MRIDNo.: NA 40 CFR: 180.624 

FROM: Bayazid H. Sarkar, Mathematical Statistician e~~ J_ ~~ Chemistry and Exposure Branch U 

Health Effects Division (r,) 

,.1 ..! rj jj ;/'{ Zldl_o .V ( r· 

David J. Miller, Chief [ ' t fJ/.·(;· 1(ft~ v 

Chemistry and Exposur~J3ranch 

THROUGH: 

Health Effects Division (7509P) 

TO: Dennis McNeilly, Chemist and Risk Assessor 

Risk Assessment Branch II 

Health Effects Division (7509P) 

And 

Andrew Ertman/Barbara Madden (RM05) 

Risk Integration, Minor Use, Emergency Response Branch 

Registration Division (7505P) 

Page 1 of8 
Background 

HEDIRABII is reviewing a request from the Interregional Research Project No. 4 (IR-4) to 

establish tolerances for residues of the fungicide metrafenone in hops. The data are the subject of 

a joint review project with Health Canada's Pest Management Regulatory Agency (PMRA). The 

submitted hops data included field trials conducted in both North America and in Europe. There 

are currently no established tolerances or maximum residue limits (MRLs) for metrafenone in hops 

in the US, Canada or Codex Alimentarius (Codex). The registrant (BASF) and IR4 submitted 

information pertaining to field trial ON05 (conducted in Ontario), including issues pertaining to 

adverse weather conditions (hurricane) and excessive insect damage. As a result, BASF and IR4 

requested the Agency consider the ON05 residue to exclude it from consideration in establishing 

the tolerance due to extreme weather conditions resulting in a crop that would not be considered 

commercially viable. RAB II requested CEB evaluate the outlier analysis submitted by BASF/IR4, 

and to determine whether the cited residue value can appropriately be discarded. 

Conclusion 

The Health Effects Division (HED) has reviewed the petitioner's submission entitled "Amendment 

to submission of the IR-4 metrafenone (3-bromo-6-methoxy-2-methylphenyl)(2,3,4-trimethoxy6-methylphenyl) methanone study on Hops" submitted on June 27, 2014 by IR4/petitioner 

supporting the outlier justification for metrafenone residue data on hops. HED also examined the 

sensitivity (using several different scenarios) of the OECD Calculation Procedures to the ON05 

hops trial data. Based on 1) the result of outlier test; 2) the sensitivity of the tolerance calculation 

to inclusion of various data sets (e.g. with and without consideration of the cited residue value and 

EU residue data of dried hops for the same pesticide); and 3) the extreme conditions reported 

during the ON05 field trial that resulted in a crop that was not commercially viable, HED/CEB has 

determined that the residue from the ON05 trial can be justifiably excluded from the MRL 

calculations for hops. 

Detailed Considerations 

Grubbs' Outlier Test 

In the analysis submitted by the petitioner/IR-4, Grubbs' test was conducted to determine whether 

the residue value from ON05 trial is an outlier. The petitioner also demonstrated that the cited 

residue value from the ON05 trial is greater than the sum of the third quartile, and three times the 

interquartile range. Based on the result of Grubbs' test and the interquartile test, the petitioner 

concluded that the residue value from the ON05 trial is an outlier and should be excluded from the 

MRL calculation. 

However,_the petitioner has conducted Grubbs' test without verifying/validating the assumption 

of normality ofthe dried hops residue data. An assumption of normality of the underlying data is 

one of the criteria that must be met in order to make valid inference from Grubb's test. If the 

underlying data do not satisfy the normality assumption it is not appropriate to conduct Grubbs' 

outlier test on data without appropriate transformation. For example, according toe-Handbook of 

Statistical Method by National Institute of Standards and Technology (NIST), Grubbs' test is used 

Page 2 of8 
to detect a single outlier in a univariate dataset that follows an approximate normal distribution. 

Therefore assumption of normality should be tested before conducting Grubbs' test. 

We note that residue data are not commonly assumed to be sampled from a Normal distribution 

because residue distribution usually contains a heavy tail and is right skewed which are not 

characteristics of the Normal distribution. Shapiro-Wilk test for normality or some other 

appropriate test should be conducted to determine whether the data satisfy the normality 

assumption. If the data are demonstrated to be non-normal by failing the normality test, the 

appropriate transformations of the data are required to make the data approximately normal prior 

to conducting Grubb's test. 

When HED evaluated the hops data for normality, there was sufficient evidence to reject the 

assumption of normality [P value (0.002) for the Shapiro-Wilk test for normality is less than 0.05 

for the untransformed residue data. See the appendix for R output] 

HED then log-transformed the registrant data on hops and the Shapiro-Wilk test failed to reject 

the normality of the transformed data, which is the null hypothesis (p-value of0.07). Hence there 

is not sufficient evidence to reject the assumption of normality for the transformed data at 

significance level 0.05. Therefore HED conducted Grubbs' test on transformed data: 

Original residue data: 

16.8699, 21.89205, 12.71335, 131.92473, 23.52335 

Log Transformed data: 

2.825531, 3.086124,2.542653,4.882232,3.157994 

Mean of the log-transformed data: 

3.298906 

Standard Deviation (SD) of the log-transformed data: 

0.9176131 

Grubbs' test was conducted using log transformed data to test whether 4.882232 which is the log 

of 131.92473 (the sample from the ON05 field trial) is an outlying observation from the rest of 

the data: 

Grubbs' Test statistic = lxm- xl = ,

4

.

882232 - 29890~ = 1.725483 

s 0.9176131 

Significance level = 0.05 

Critical value1 is 1.715. (See Appendix for calculation of critical value using R) 

1 the formula to derive the critical value is provided in the e-Handbook of Statistical method by NIST 

Page 3 of8 
Since the test statistic is greater than the critical value, there is sufficient evidence to reject the 

null hypothesis and conclude that 4.882232 which is log(131.92473) is in fact outlier at 0.05 

level. 

Although HED reached the same conclusion as the petitioner about the outlying observation of 

the ON05 trial residue in dried hops, HED believes it is more appropriate to conduct the Grubbs' 

test using log-transformed data for dried hops (as was done above). 

Interquartile Range (IQR) Test: 

HED reviewed the IQR test performed by the petitioner!IR4 and found that the cited residue value 

from the ON05 trial is indeed greater than the sum of the third quartile and three times the inter 

quartile range (i.e. IQR). According to the NIST e- Handbook of Statistical Method, a point greater 

than the sum of the third quartile and three times the inter quartile range (i.e. Q3 + 3*IQR) is 

considered an extreme outlier. However it should be noted that the IQR criterion is not a robust 

measure of outlier when the data are derived from a skewed distribution2

• Residue data often follow 

right skewed distribution (e.g. lognormal). Therefore, the result from IQR test may not be 

considered as confirmatory by itself. It can be used in conjunction with other tests to decide the 

outlying observation. 

Tolerance/MRL Calculation Considerations: 

Sensitivity of ON05 field trial in Tolerance calculation and comparison with EU data: 

It was noted that the highest residues in dried hops cones were observed in samples from the ON05 

trial where the second application was delayed due to a hurricane and where, according to the 

petitioner, half of the crop was destroyed by insects between applications3• 

In addition, HED examined the sensitivity of ON05 hops field trial residue in the tolerance 

calculation. HED determined that if the ON05 trial result is included in the tolerance calculation 

(using the OECD MRL calculation procedure) then the tolerance value is five times larger than 

the value calculated excluding the ON05 trial result, i.e., yielding a value of 300 ppm including 

the data, and 60 ppm excluding the data. HED also determined the tolerance value relying only 

on European data for dried hops, resulting in a tolerance of 80 ppm. The MRL calculated using 

European data is close to the MRL derived from NAFTA field trial data excluding the sample from 

the ON05 trial. HED also determined the MRL resulting from use of combined data sets 

(NAFTA+ EU and excluding ON05 trial), resulting in a value of70 ppm. 

HED also noted that if the ON05 residue is included in the calculation of the highest (per trial 

average) residue value for dried hops, the NAFT A result is much higher than the European result 

[131.9 ppm (NAFTA) vs. 34.4 ppm EU)]. However the distribution of residue values is 

approximately comparable if the ON05 trial is excluded. 

2 Mia Hubert and E. Vandervieren (2004) An adjusted boxplot for skewed distributions. 3 see EPA DER ofMRID No. 49145403 (metrafenone on hops) for details [Drew, D411389, 7/7/14] 

Page 4 of8 
Case 1) Calculate the MRL using all data from the NAFTA field trials: 

NAFTA Field Trial residues= 16.8699,21.89205, 12.71335, 131.92473,23.52335 

MRL=300. 

Case 2) Calculate the MRL using all data from NAFTA field trial except the ON05 trial results: 

NAFTA Field Trial residues= 16.8699, 21.89205, 12.71335, 23.52335 

MRL=60 

Case 3) Calculate the MRL using EU dried hops residue data: 

EU Dried hops residues: 21.3, 22.8, 13.3, 19.7, 34.4, 33.05 

MRL=80 

Case 4) Calculate the MRL using EU + NAFTA hops data excluding the ON05 trial results: 

NAFTA + EU residues: 16.8699,21.89205, 12.71335,23.52335,21.3, 22.8, 13.3, 19.7, 

34.4, 33.05 

MRL=70 

[Note: All MRLs were calculated using the OECD MRL calculation procedures] 

Conclusion: 

Based on the result of the outlier test, combined with consideration of the EU data in addition to 

the various "what if' tolerance calculations and the fact that the ON05 field trial was subject to 

unusual conditions, HED has concluded that a more reasonable tolerance can be calculated by 

eliminating the outlier value. 

References: 

Amendment to submission of the IR-4 metrafenone (3-bromo-6-methoxy-2-methylphenyl)(2,3,4-

trimethoxy-6-methylphenyl) methanone study on Hops" submitted on June 27, 2014 

NISTISEMATECH e-Handbook of Statistical Method, Grubbs' Test for Outliers: 

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h1.htm , date: 08/11/2014 

Mia Hubert and E. Vandervieren (2004) An adjusted boxplotfor skewed distributions. 

COMPSTAT'2004 Symposium 

D407432, D411389, D414077, D416465, D417602, William T. Drew: Jul 7, 2014. 

Metrafenone. Petition to Establish Permanent Tolerances (with Section 3 Registration) for 

Page 5 of8 
Residues Resulting from Use of the Fungicide on Apricot, Cherry Subgroup 12-12A, Peach 

Subgroup 12-12B, Fruiting Vegetables Group 8-10, Small Vine Climbing Fruit Except Fuzzy 

Kiwifruit Subgroup 13-07F, Hops, Pome Fruits Group 11-10, and Cucurbit Vegetables Group 9; 

Evaluation of Conditional Data. Summary of Analytical Chemistry and Residue Data. 

Appendix 

#R output for Shapiro-Wilk Normality test: 

> # Untransformed residue data 

> y = c(16.8699, 21.89205, 12.71335, 131.92473, 23.52335) 

> 

> shapiro.test (y) 

Shapiro-Wilk normality test 

data: y 

W = 0.6333, p-value = 0.001673 

> #log-transformed residue data 

> 

> z = c(2.825531, 3.086124,2.542653,4.882232, 3.157994) 

> 

> 

> shapiro.test (z) 

Shapiro-Wilk normality test 

Page 6 of8 
data: z 

W = 0. 7939, p-value = 0.07222 

# R code for Grubbs' test critical value calculation : 

Grubbs<- function( N, alpha){ 

g =alpha I (2*N) 

a= qt(g, df=N-2) 

b = ((N-1)/sqrt(N)) * sqrt(a"2/(N-2 +a"2)) 

return (b) 

} 

> y1 = Grubbs(5, 0.05) 

>y1 

[1] 1.715037 

##### IQR Criterion#### 

> quantile (y) 

0% 25% 50% 75% 100% 

12.71335 16.86990 21.89205 23.52335 131.92473 

> IQR(y) 

Page 7 of8 
[1] 6.65345 

> 3*IQR(y) 

[1] 19.96035 

> 23.52335 + 3*IQR(y) 

[I] 43.4837 

Page 8 of8