PEER REVIEW DRAFT - DO NOT CITE OR QUOTE
«EPA
United States
Environmental Protection Agency
EPA Document #740-Rl-8013
August 2019, DRAFT
Office of Chemical Safety and
Pollution Prevention
Draft Risk Evaluation for
1-Bromopropane
(w-Propyl Bromide)
CASRN: 106-94-5
Supplemental Information on Human Health Benchmark Dose Modeling
August 2019
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PEER REVIEW DRAFT - DO NOT CITE OR QUOTE
TABLE OF CONTENTS
TABLE OF CONTENTS 2
LIST OF TABLES 4
LIST OF FIGURES 9
ACKNOWLEDGEMENTS 12
1 INTRODUCTION 13
2 BENCHMARK DOSE MODELING OF NON-CANCER EFFECTS 13
2.1 Benchmark Dose Modeling of Non-Cancer Effects for Acute Exposures 13
2.1.1 Decreased Live Litter Size 13
2.1.2 Post implantation loss 18
2.2 Benchmark Dose Modeling of Non-Cancer Effects for Chronic Exposures 27
2.2.1 Increased Incidence of Vacuolization of Centrilobular Hepatocytes in Males 27
2.2.2 Increased Incidence of Vacuolization of Centrilobular Hepatocytes in Males 29
2.2.3 Increased Incidence of Vacuolization of Centrilobular Hepatocytes in Females 32
2.2.4 Increased Incidence of Renal Pelvic Mineralization in Males 35
2.2.5 Increased Incidence of Renal Pelvic Mineralization in Females 38
2.2.6 Decreased Seminal Vesicle Weight 40
2.2.6.1 Decreased Relative Seminal Vesicle Weight 41
2.2.6.2 Decreased Absolute Seminal Vesicle Weight 43
2.2.7 Decreased Percent Normal Sperm Morphology 46
2.2.8 Decreased Percent Motile Sperm 49
2.2.9 Decreased Left Cauda Epididymis Weight 51
2.2.10 Decreased Right Cauda Epididymis Weight 54
2.2.11 Increased Estrus Cycle Length 57
2.2.12 Decreased Antral Follical Count 59
2.2.13 Decreased Male and Female Fertility Index 59
2.2.14 Decreased Implantations Sites 62
2.2.15 Decreased Pup Body Weight 66
2.2.15.1 Decreased Body Weight in F1 Male Pups at PND 28 66
2.2.15.2 Decreased Body Weight in F2 Female Pups at PND 14 72
2.2.15.3 Decreased Body Weight in F2 Female Pups at PND 21 75
2.2.15.4 Decreased Body Weight in F2 Male Pups at PND 14 77
2.2.15.5 Decreased Body Weight in F2 Male Pups at PND 21 80
2.2.16 Decreased Brain Weight 83
2.2.16.1 Decreased Brain Weight in Fo Females 83
2.2.16.2 Decreased Brain Weight in Fo Males 85
2.2.16.3 Decreased Brain Weight in F1 Females as Adults 88
2.2.16.4 Decreased Brain Weight in F1 Males as Adults 90
2.2.16.5 Decreased Brain Weight in F2 Females at PND 21 92
2.2.16.6 Decreased Brain Weight in F2 Males at PND 21 95
2.2.17 Decreased Hang Time 98
3 BENCHMARK DOSE MODELING OF TUMORS 101
3.1 Lung Tumors in Female Mice 102
3.1.1 Summary of Multistage Model 105
3.1.1.1 Selected Frequentist Multistage - Multistage 1 Restricted; Extra Risk, BMR = 0.001 and 0.1, doses are in ppm
105
3.1.1.2 Selected Frequentist Multistage - Multistage 1 Restricted; Added Risk, BMR = 0.001 and 0.1, doses are in ppm
108
3.1.2 Summary of Frequentist Model Averaging 110
3.1.3 Summary of Bavesian Model Averaging Ill
3.1.3.1 Bayesian Model Averaging - Extra Risk, BMR = 0.001 and 0.1, doses are in ppm Ill
3.1.3.2 Bayesian Model Averaging - Added Risk, BMR = 0.001 and 0.1, doses are in ppm 112
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Table 2-19 Summary of BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in
Female Fo Rats Following Inhalation Exposure to 1-BP in a Two-Generation Study 33
Table 2-20 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Female Rats
Exposed to 1-BP Via Inhalation; BMR 10% Added Risk 34
Table 2-21 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for
1-BP 36
Table 2-22 Summary of BMD Modeling Results for Renal Pelvic Mineralization in Male Fo Rats
Following Inhalation Exposure to 1-BP in a Two-Generation Study 36
Table 2-23 BMD Modeling Results for Renal Pelvic Mineralization in Male Rats Exposed to 1-BP
Via Inhalation; BMR 10% Added Risk 37
Table 2-24 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for
1-BP 38
Table 2-25 Summary of BMD Modeling Results for Renal Pelvic Mineralization in Female Fo Rats
Following Inhalation Exposure to 1-BP in a Two-Generation Study 39
Table 2-26 BMD Modeling Results for Renal Pelvic Mineralization in Female Rats Exposed to 1-
BP Via Inhalation; BMR 10% Added Risk 39
Table 2-27 Relative Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP
41
Table 2-28 Summary of BMD Modeling Results for Relative Seminal Vesicle Weight in Rats
Exposed to 1-BP by Inhalation 41
Table 2-29 BMD Modeling Results for Relative Seminal Vesicle Weight; BMR = 1 Standard
Deviation Change from Control Mean 42
Table 2-30 Absolute Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP
Table 2-31 Summary of BMD Modeling Results for Seminal Vesicle Absolute Weight in Rats
Exposed to 1-BP by Inhalation 44
Table 2-32 BMD Modeling Results for Seminal Vesicle Absolute Weight; BMR = 1 Standard
Deviation Change from Control Mean 45
Table 2-33 Sperm Morphology Data Selected for Dose-Response Modeling for 1-BP 46
Table 2-34 Summary of BMD Modeling Results for Sperm Morphology in the Fo Generation
Exposed to 1-BP by Inhalation 47
Table 2-35 BMD Modeling Results for Sperm Morphology in Fo Rats Exposed to 1-BP by
Inhalation; BMR = 1 Standard Deviation Change from Control Mean 48
Table 2-36 Sperm Motility Data Selected for Dose-Response Modeling for 1-BP 49
Table 2-37 Summary of BMD Modeling Results for Sperm Motility Fo Male Rats Following
Inhalation Exposure to 1-BP 50
Table 2-38 Summary of BMD Modeling Results for Sperm Motility Fo Male Rats Following
Inhalation Exposure to 1-BP with the Highest Dose Dropped 51
Table 2-39 Left Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling
for 1-BP 52
Table 2-40 Summary of BMD Modeling Results for Left Cauda Epididymis Absolute Weight Fo
Male Rats Following Inhalation Exposure to 1-BP 52
Table 2-41 BMD Modeling Results for Left Cauda Epididymis Absolute Weight; BMR = 1
Standard Deviation Change from Control Mean 53
Table 2-42 Right Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling
for 1-BP 55
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Table 2-43 Summary of BMD Modeling Results for Right Cauda Epididymis Absolute Weight Fo
Male Rats Following Inhalation Exposure to 1-BP 55
Table 2-44 BMD Modeling Results for Right Cauda Epididymis Absolute Weight; BMR = 1
Standard Deviation Change from Control Mean 56
Table 2-45 Estrus Cycle Length Data Selected for Dose-Response Modeling for 1-BP 58
Table 2-46 Summary of BMD Modeling Results for Estrus Cycle Length Fo Female Rats
Following Inhalation Exposure to 1-BP 58
Table 2-47 Antral Follicle Count Data Selected for Dose-Response Modeling for 1-BP 59
Table 2-48 Summary of BMD Modeling Results for Antral Follical Count in Female Rats
Following Inhalation Exposure to 1-BP 59
Table 2-49 Fertility Index Data Selected for Dose-Response Modeling for 1-BP 60
Table 2-50 Summary of BMD Modeling Results for Fertility Index of Fo Rats Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study 60
Table 2-51 BMD Modeling Results for Fertility Index in Rats Exposed to 1-BP Via Inhalation
BMR 10% Extra Risk 61
Table 2-52 Implantations Site Data Selected for Dose-Response Modeling for 1-BP 62
Table 2-53 Summary of BMD Modeling Results for Implantations Sites in Fo Rats Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 63
Table 2-54 BMD Modeling Results for Implantation Sites in Rats Exposed to 1-BP Via Inhalation
in ppm BMR 1 Standard Deviation 64
Table 2-55 Pup Body Weight Data in Fi Males at PND 28 for Dose-Response Modeling 66
Table 2-56 Summary of BMD Modeling Results for Body Weight of Fi Male Rat Pups on PND 28
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 67
Table 2-57 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation
BMR 5% Relative Deviation 68
Table 2-58 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation
BMR 5% Relative Deviation 70
Table 2-59 Pup Body Weight Data in F2 Females at PND 14 from Selected for Dose-Response
Modeling 72
Table 2-59 Summary of BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND
14 Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 72
Table 2-60 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study with Variances Fixed at
Smallest, Pooled and Highest Values 74
Table 2-61 Pup Body Weight Data in F2 Females at PND 21 from Selected for Dose-Response
Modeling 75
Table 2-62 Summary of BMD Modeling Results for Body Weight of F2 Females on PND 21
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 75
Table 2-63 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation
BMR = 5% Relative Deviation 76
Table 2-64 Pup Body Weight Data in F2 Males at PND 14 from Selected for Dose-Response
Modeling 77
Table 2-65 Summary of BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 14
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 78
Table 2-66 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation
in ppm BMR = 5% Relative Deviation 79
Table 2-67 Pup Body Weight Data in F2 Males at PND 21 80
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Table 2-68 Summary of BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 21
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 81
Table 2-69 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation
in ppm BMR = 5% Relative Deviation 81
Table 2-70 Brain Weight Data in Fo Females for Dose-Response Modeling 83
Table 2-71 Summary of BMD Modeling Results for Brain Weight of Fo Females Following
Inhalation Exposure to 1-BP 83
Table 2-72 BMD Modeling Results for Brain Weight in Fo Female Rats Exposed to 1-BP Via
Inhalation in ppm BMR = 1 Standard Deviation 84
Table 2-73 Brain Weight Data in Fo Males for Dose-Response Modeling 85
Table 2-74 Summary of BMD Modeling Results for Brain Weight of Fo Males Following
Inhalation Exposure to 1-BP 86
Table 2-75 BMD Modeling Results for Brain Weight of Fo Male Rats Following Inhalation
Exposure to 1-BP in a Two-Generation Study with Variances Fixed at Smallest, Pooled and
Highest Values 87
Table 2-76 Brain Weight Data in Fi Females as Adults from Selected for Dose-Response Modeling
88
Table 2-77 Summary of BMD Modeling Results for Brain Weight of Fi Female Rats as Adults
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 88
Table 2-78 BMD Modeling Results for Brain Weight in Fi Female Rats as Adults Exposed to 1-BP
Via Inhalation BMR = 1% Relative Deviation 89
Table 2-79 Brain Weight Data in Fi Males as Adults from Selected for Dose-Response Modeling
91
Table 2-80 Summary of BMD Modeling Results for Brain Weight of Fi Male Rats as Adults
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 91
Table 2-81 Brain Weight Data in F2 Females at PND 21 from Selected for Dose-Response
Modeling 92
Table 2-82 Summary of BMD Modeling Results for Brain Weight of F2 Female Rats at PND 21
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 92
Table 2-83 BMD Modeling Results for Brain Weight in F2 Female Exposed to 1-BP Via Inhalation
BMR = 1% Relative Deviation 93
Table 2-84 Brain Weight Data in F2 Males at PND 21 for Dose-Response Modeling 95
Table 2-85 Summary of BMD Modeling Results for Brain Weight of F2 Male Rats as Adults
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study 95
Table 2-86 BMD Modeling Results for Brain Weight in Rats Exposed to 1-BP Via Inhalation in
ppm BMR = 1% Relative Deviation 96
Table 2-87 Hang Time from a Suspended Bar Data for Dose-Response Modeling for 1-BP 98
Table 2-88 Summary of BMD Modeling Results for Hang Time from a Suspended Bar; BMR = 1
std. dev. change from control mean 98
Table 2-89 BMD Modeling Results for Hang Time from a Suspended Bar; BMR = 1 Standard
Deviation Change from Control Mean 99
Table 3-1 Incidence of Lung Tumors in Female Mice 102
Table 3-2 Summary of BMDS 3.0 modeling results for lung tumors in female mice exposed to 1-
BP by inhalation for 2 years (NTP, 2011); BMRs = 10% and 0.1% extra and added risk, doses are
in ppm 103
Table 3-3 Lung Tumors in Female Mice, Selected Frequentist Multistage - Multistage 1 Restricted;
Extra Risk, BMR = 0.001 and 0.1 User Input 105
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Table 3-4 Lung Tumors in Female Mice, Selected Frequentist Multistage - Multistage 1 Restricted;
Extra Risk, BMR = 0.001 and 0.1 Model Results 105
Table 3-5 Lung Tumors in Female Mice, Selected Frequentist Multistage - Multistage 1 Restricted;
Added Risk, BMR = 0.001 and 0.1 User Input 108
Table 3-6 Lung Tumors in Female Mice, Selected Frequentist Multistage - Multistage 1 Restricted;
Added Risk, BMR = 0.001 and 0.1 Model Results 108
Table 3-7 Lung Tumors in Female Mice, Summary of Frequentist Model Averaging 110
Table 3-8 Lung Tumors in Female Mice, Bayesian Model Averaging - Extra Risk, BMR = 0.001
and 0.1 User Inputs Ill
Table 3-9 Lung Tumors in Female Mice, Bayesian Model Averaging - Extra Risk, BMR = 0.001
and 0.1 Model Results Ill
Table 3-10 Lung Tumors in Female Mice, Bayesian Model Averaging - Added Risk, BMR =
0.001 and 0.1 User Inputs 112
Table 3-11 Lung Tumors in Female Mice, Bayesian Model Averaging - Added Risk, BMR =
0.001 and 0.1 Model Results 112
Table 3-12 Incidence of Large Intestine Adenomas in Female Rats 113
Table 3-13 Summary of BMDS 3.0 modeling results for large intestine adenomas in female rats
exposed to 1-BP by inhalation for 2 years (NTP, 2011); BMRs = 10% and 0.1% extra and added
risk, doses are in ppm 114
Table 3-14 Large Intestine Adenomas in Female Rats, Selected Frequentist Multistage - Multistage
1 Restricted; Extra Risk, BMR = 0.001 and 0.1 User Input 116
Table 3-15 Large Intestine Adenomas in Female Rats, Selected Frequentist Multistage - Multistage
1 Restricted; Extra Risk, BMR = 0.001 and 0.1 Model Results 116
Table 3-16 Large Intestine Adenomas in Female Rats, Selected Frequentist Multistage - Multistage
1 Restricted; Added Risk, BMR = 0.001 and 0.1 User Input 119
Table 3-17 Large Intestine Adenomas in Female Rats, Selected Frequentist Multistage - Multistage
1 Restricted; Added Risk, BMR = 0.001 and 0.1 Model Results 119
Table 3-18 Large Intestine Adenomas in Female Rats, Summary of Frequentist Model Averaging
120
Table 3-19 Large Intestine Adenomas in Female Rats, Bayesian Model Averaging - Extra Risk,
BMR = 0.001 and 0.1 User Inputs 121
Table 3-20 Large Intestine Adenomas in Female Rats, Bayesian Model Averaging - Extra Risk,
BMR = 0.001 and 0.1 Model Results 121
Table 3-21 Large Intestine Adenomas in Female Rats, Bayesian Model Averaging - Added Risk,
BMR = 0.001 and 0.1 User Inputs 122
Table 3-22 Large Intestine Adenomas in Female Rats, Bayesian Model Averaging - Added Risk,
BMR = 0.001 and 0.1 Model Results 122
Table 3-23 Incidence of Keratoacanthoma and Squamous Cell Carcinomas in Male Rats 123
Table 3-24 Summary of BMDS 3.0 modeling results for keratoacanthoma & squamous cell
carcinomas in male rats exposed to 1-BP by inhalation for 2 years (NTP, 2011); BMRs = 10% and
0.1% extra and added risk, doses are in ppm 124
Table 3-25 Keratoacanthoma and Squamous Cell Carcinomas in Male Rats, Selected Frequentist
Multistage - Multistage 1 Restricted; Extra Risk, BMR = 0.001 and 0.1 User Input 126
Table 3-26 Keratoacanthoma and Squamous Cell Carcinomas in Male Rats, Selected Frequentist
Multistage - Multistage 1 Restricted; Extra Risk, BMR = 0.001 and 0.1 Model Results 126
Table 3-27 Keratoacanthoma and Squamous Cell Carcinomas in Male Rats, Selected Frequentist
Multistage - Multistage 1 Restricted; Added Risk, BMR = 0.001 and 0.1 User Input 128
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Figure 2-13 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model
with Constant Variance for Relative Seminal Vesicle Weight; BMR = 1 Standard Deviation
Change from Control Mean 42
Figure 2-14 Plot of Mean Response by Dose in ppm with Fitted Curve for Hill Model with
Constant Variance for Seminal Vesicle Absolute Weight; BMR = 1 Standard Deviation Change
from Control Mean 45
Figure 2-15 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M2) Model
with Constant Variance for Sperm Morphology in Fo Rats Exposed to 1-BP by Inhalation; BMR =
1 Standard Deviation Change from Control Mean 48
Figure 2-16 Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model
with Constant Variance for Left Cauda Epididymis Absolute Weight; BMR = 1 Standard Deviation
Change from Control Mean 53
Figure 2-17 Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model
with Constant Variance for Right Cauda Epididymis Absolute Weight; BMR = 1 Standard
Deviation Change from Control Mean 56
Figure 2-18 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(LogLogistic) for Fertility Index in Rats Exposed to 1-BP Via Inhalation in ppm BMR 10% Extra
Risk 61
Figure 2-19 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Implantation Sites in Rats Exposed to 1-BP Via Inhalation in ppm BMR 1 Standard Deviation. ...64
Figure 2-20 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR 5%
Relative Deviation 68
Figure 2-21 Plot of Mean Response by Dose with Fitted Curve for the Hill Model for Pup Body
Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR 5% Relative Deviation 70
Figure 2-21 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Polynomial 2°) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5%
Relative Deviation 76
Figure 2-22 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Polynomial 2°) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5%
Relative Deviation 78
Figure 2-23 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative Deviation.
81
Figure 2-24 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Brain Weight in Fo Female Rats Exposed to 1-BP Via Inhalation in ppm BMR = 1 Standard
Deviation 84
Figure 2-25 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Brain Weight in Fi Female Rats as Adults Exposed to 1-BP Via Inhalation
in ppm BMR = 1% Relative Deviation 89
Figure 2-26 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Brain Weight in F2 Female Exposed to 1-BP Via Inhalation in ppm BMR =
1% Relative Deviation 93
Figure 2-27 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Power) for
Brain Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 1% Relative Deviation 96
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Figure 2-28 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model
with Modeled Variance for Hang Time from a Suspended Bar; BMR = 1 Standard Deviation
Change from Control Mean 99
Figure 3-1 Plot of Results for Lung Tumors in Female Mice Frequentist Multistage Degree 1
Model with BMR of 10% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the
BMDL 107
Figure 3-2 Plot of Results for Large Intestine Adenomas in Female Rats Frequentist Multistage
Degree 1 Model with BMR of 10% Extra Risk for the BMD and 0.95 Lower Confidence Limit for
the BMDL 118
Figure 3-3 Plot of Results for Keratoacanthoma and Squamous Cell Carcinomas in Male Rats
Frequentist Multistage Degree 1 Model with BMR of 10% Extra Risk for the BMD and 0.95
Lower Confidence Limit for the BMDL 127
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31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
means were well-modeled the variances are not well modeled by the non-homogeneous variance
model (the non-homogeneous variance model was used because the BMDS test 2/>-value =
0.0130). To investigate the effect of the poor modeling of the variances on the BMDL, the models
were run using the smallest dose standard deviation (2.21), highest (4.47) and pooled (3.54) for all
dose levels and the results are summarized in Table 2-4. As shown in the last column of Table 2-4
the ratios BMDLs for the lowest to the highest variance for the two best fitting models the Linear
and Exponential (M2) models are 1.15 and 1.20, respectively. Overall the adjustment of the
variances from most-variable to least-variable for all of the models makes little difference on the
BMDL. This is strong evidence that the poor variance modeling for the original data is not
substantially impacting the BMDL estimates. It is reasonable to use the non-homogeneous
Exponential M2 model for the original data because it has the lowest AIC of all the model choices
for the original data and therefore a BMDL of 41 ppm (40.7 ppm rounded to two significant
figures) was selected for this endpoint.
Table 2-2 Summary of BMD Modeling Results for Reduced Litter Size in Fo Generation
Exposed to 1-BP by Inhalation; BMRs of 1 Standard Deviation, and 5% and 1% Relative
Deviation From Control Mean.
Model"
Goodness of
fit
BMD
1SD
(ppm)
BMDL
1SD
(ppm)
BMD
5RD
(ppm)
BMDL
5RD
(ppm)
BMD
1RD
(ppm)
BMDL
1RD
(ppm)
Basis for model
selection
/>-value
AIC
Exponential (M2)
Exponential (M3)b
0.533
291.
10
256 ^
158
61.3
40.7
12.0
7.97
The Exponential
(M2) model was
selected based on
lowest AIC from
this set of models
which have
adequate /7-values,
adequate fit by
visual inspection
and the BMDLs
are < 4-fold apart
considered
sufficiently close.
Power0
Polynomial 3od
Polynomial 2oe
Linear
0.433
291.
51
281
189
69.9
49.8
14.0
9.95
Hill
0.722
291.
96
178
error8
35.8
10.4
6.36
1.69
Exponential (M4)
Exponential (M5)f
0.622
292.
08
181
69.4
40.4
17.8
7.48
3.23
a Modeled variance case presented (BMDS Test 2 p-value = 0.0130), selected model in bold; scaled residuals for selected
model for doses 0,100,250, and 500 ppm were -0.16, -0.05, 0.66, -0.76, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). Hie models in this row reduced to the Exponential (M2)
model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
g BMDL computation failed for this model.
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Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
lnalpha
10.4606
6.08025
rho
-3.14328
-1.44632
a
14.4915
10.5312
b
0.000836398
0.00102437
c
n/a
0
d
n/a
1
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
23
14.4
14.49
2.21
2.8
-0.1569
100
25
13.3
13.33
3.72
3.19
-0.04505
250
22
12.3
11.76
4.47
3.88
0.6554
500
11
8.3
9.54
4.1
5.4
-0.7614
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
-143.3786
5
296.7571
A2
-137.9879
8
291.9758
A3
-140.9173
6 1
293.8347
R
®53.5054
2
311.0108
2
-141.5475
4
291.095
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1^
31.03
6
<0.0001
Test 2
10.78
3
0.01297
Test 3
5.859
2
0.05343
Test 4
1.26
2
0.5325
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58
59
60
Table 2-4 BMD Modeling Results for Reduced Litter Size in Fo Generation Following Inhalation Exposure of Parental Rats to
Model"
Smallest Standard Deviation
Pooled Standard Deviation
Largest Standard Deviation
Ratio
BMDLs
Smallest
to Largest
Std Dev
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
/>-value
AIC
/>-value
AIC
/j-valuc
AIC
Linear
0.279
213.92
63.5
53.5
0.605
288.69
63.5
49.2
0.729
326.11
63.5
46.6
1.15
Exponential
(M2)
0.112
215.74
54.9
44.1
0.420
289.42
54.9
39.4
0.579
326.57
54.9
36.7
1.20
Exponential
(M4)
0.112
215.74
54.9
42.6
0.420
289.42
54.9
34.4
0.579
326.57
54.9
29.1
1.46
Polynomial 3°
0.506
213.81
96.4
58.4
0.678^
289.86
96.4
51.1
0.742
327.58
96.4
47.8
1.22
Polynomial 2°
0.393
214.09
105
57.4
0.593
289.97
105
50.8
0.672
327.65
105
47.6
1.21
Power
0.303
214.43
115
56.4
0.519
290.10
115
50.5
0.609
327.74
115
47.4
1.19
Exponential
(M3)
0.239
214.75
127
56.1
0.461
290.23
127
42.6
0.559
327.82
127
38.7
1.45
Exponential
(M5)
0.239
214.75
127
56.1
N/AbL
292.23
127
42.6
0.559
327.82
127
33.0
1.70
Hill
N/Ab
216.43
115
56.4
N/Ab
292.10
116
50.3
N/Ab
329.74
116
47.2
1.19
61
62
a Constant variance case presented (BMDS Test 2 />-value = 1.000, BMDS Test 3 />-value = 1.000), no model was selected as a best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
-------
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
500
500
Number of Implantation Sites
14
16
14
14
15
16
12
16
16
14
18
16
16
15
15
15
12
18
16
16
15
15
17
14
15
13
15
17
16
16
11
15
12
18
18
12
12
Post Implantation Loss
1
Dam Weight at Study Week 0 (g)
152
165
166
158
168
143
148
177
154
153
179
171
180
170
165
157
164
162
159
160
151
141
179
150
153
175
146
161
167
165
166
162
157
153
158
166
167
146
164
155
161
158
-------
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
Dose (ppm)
Number of Implantation Sites
Post Implantation Loss
Dam Weight at Study Week 0 (g)
500
5
1
181
500
15
2
159
500
12
1
151
500
16
0
152
500
9
1
166
500
6
0
176
500
6
1
165
500
11
0
144
500
2
0
144
The application of nested dichotomous models to these data was possible because the incidence
data for post-implantation loss were available for every litter, and preferable because they can
account for intra-litter correlations and litter-specific covariates. A litter specific covariate that is
potentially related to the endpoint of concern but is not itself impacted by dose is needed for this
analysis. In this case, dam body weight measured at week 0 and the number of implantation sites
were both used as covariates and the data was modeled separately in the same format for each. In
this case, dam body weight measured at week 0 was selected as the preferred litter specific
covariate because it was not affected at any dose and is potentially related to the implantation
loss endpoint.
Incidence of implantation loss presented a clear dose trend at lower doses but leveled off at the
highest dose coincident with a reduction in implantation sites. The data were modeled with the
all doses and the highest dose dropped for the purposes of this analysis because of the
uncertainty associated with reduced sample size and improved model fit.
The nested modeling was performed using the nested logistic and NCTR models contained in
BMDS 2.7.0.4, as follows:
• nested model for extra risk of 5% and 1%, using dam weight as a litter specific covariate,
dropping the highest dose group (Table 2-6 and Table 2-7 and Figure 2-2 and Figure 2-3).
• nested model for extra risk of 5% and 1%, using number of implantation sites as a litter
specific covariate, dropping the highest dose group (Table 2-8 and Table 2-9 and Figure
2-4 and Figure 2-5).
• nested model for extra risk of 5% and 1%, using dam weight as a litter specific covariate,
including all dose groups (Table 2-10 and Table 2-11 and Figure 2-6 and Figure 2-7).
-------
184
185
186
187
188
189
190
191
192
193
194
195
196
Table 2-15 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-
Response Modeling for 1-BP
Dose (ppm)
Number of animals
Incidence
0
15
0
100
15
0
200
15
0
400
15
3
800
15
6
The BMD modeling results for vacuolization of centrilobular hepatocytes are summarized in
Table 2-16. The best fitting model was the LogLogistic based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit /rvalue (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure 2-9. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below in Table 2-17.
Table 2-16 Summary of BMD Modeling Results for Vacuolization of Centrilobular
Hepatocytes in Male Rats Following Inhalation Exposure to 1-1
:p
Model3
Goodness of fit
BMDlOPctAdd
(ppm)
BMDLioPctAdd
(ppm)
Basis for model selection
/>-value
AIC
Multistage 3°
( 0.955
38.189 ^
346
226
Multistage 3° model was
selected based on the lowest
AIC from this set of models
which have adequate />-valuc,
adequate fit by visual inspection
and the BMDLs are < 1.5-fold
apart considered sufficiently
close.
Multistage 2°
0.898
39.202
289
198
LogProbit
0.951
39.678
345
225
Gamma
0.919
39.874
349
227
LogLogistic
0.903
^40.003
349
224
Weibull
0.872
40.180
351
222
Probit
0.773
40.585
370
275
Logistic
0.662
41.195
382
290
a Selected model in bold; scaled residuals for selected model for doses 0, 100, 200,400, and 600 ppm were 0, -0.2, -0.56, 0.54, -
0.18, respectively.
-------
213
214
215
216
217
218
219
220
221
222
223
224
225
Table 2-18 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-
Response Modeling for 1-BP
Dose (ppm)
Number of animals
Incidence
0
25
0
100
25
0
250
25
0
500
25
6
750
25
16
The BMD modeling results for vacuolization of centrilobular hepatocytes are summarized in
Table 2-19. The best fitting model was the LogProbit based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure 2-10. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below in .
Table 2-19 Summary of BMD Modeling Results for Vacuolization of Centrilobular
Hepatocytes in Female Fo Rats Following Inhalation Exposure to 1-BP in a Two-
Generation Study
Model3
Goodness of fit
BMDlOPctAdd
(ppm)
BMDLioPctAdd
(ppm)
Basis for model selection
/>-value
AIC
LogProbit
( 0.988
64.438 ^
415
322
LogProbit model was selected
based on the lowest AIC from
this set of models which have
adequate /7-values (excluding
Quantal-Linear), adequate fit
by visual inspection and the
BMDLs are 1.5-fold apart
considered sufficiently close.
Gamma
0.965
64.648
416
320
LogLogistic
0.945 ^
64.843
415
320
Weibull
0.879
65.283
411
310
Probit
0.826
^65.496
423
335
Logistic
0.661
66.491
431
347
Multistage 2°
0.410
68.583
279
228
Quantal-Linear
0.0134
80.285
153
109
a Selected model in bold; scaled residuals for selected model for doses 0, 100, 250, 500, and 750 ppm were 0, 0, -0.29, 0.19,
-0.11, respectively.
-------
241
242
243
244
245
246
247
248
249
250
251
252
Table 2-21 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling
for 1-BP
Dose (ppm)
Number of animals
Incidence
0
25
1
100
25
0
250
25
1
500
25
2
750
25
6
The BMD modeling results for vacuolization of renal pelvic mineralization are summarized in
Table 2-22. The best fitting model was the Multistage 3° based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure 2-11. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below in Table 2-23.
Table 2-22 Summary of BMD Modeling Results for Renal Pelvic Mineralization in Male Fo
Rats Following Inhalation Exposure to 1-BP in a Two-Generation Study
Model"
Goodness of fit
BMDlOPctAdd
(ppm)
BMDLioPctAdd
(ppm)
Basis for model selection
/>-value
AIC
Multistage 3°
0.789
63.835
571
386
Multistage 3° model was
selected based on the lowest
AIC from this set of models
which have adequate /7-values,
adequate fit by visual inspection
and the BMDLs are 1.5-fold
apart considered sufficiently
close.
Multistage 2°
{ 0.668
64.258 ^
527
368
Logistic
0.629
64.260
545
434
Probit
0.567
64.488
526
408
Weibull
0.603
65.825
581
375
LogLogistic
0.602
65.835
579
371
Gamma
0.597
^65.856
575
371
LogProbit
0.597
65.894
577
355
Quantal-Linear
0.326
66.496
507
284
a Selected model in bold; scaled residuals for selected model for doses 0, 100, 250, 500, and 750 ppm were 0.6, -0.76, 0.26,
-0.18, 0.07, respectively.
-------
326
327
328
329
330
331
332
333
334
335
336
337
338
339
Table 2-30 Absolute Seminal Vesicle Weight Data Selected for Dose-Response Modeling for
1-BP
Dose (ppm)
Number of animals
Seminal Vesicle Absolute Weight (mg)
Standard Deviation
0
8
1.88
0.27
200
9
1.38
0.26
400
9
1.27
0.25
800
9
1.00
0.36
Comparisons of model fits obtained are provided in Table 2-31. Models with homogeneous
variance were used because the BMDS Test 2 /rvalue was 0.653. The best fitting model (Hill)
was selected based on Akaike information criterion (AIC; lower values indicates a better fit),
chi-square goodness of fit p-value (higher value indicates a better fit) and visual inspection. The
Hill model had an acceptable BMD to BMDL ratio of 2.5 and is indicated in bold. For the best
fitting model a plot of the model is shown in Figure 2-14. The model version number, model
form, benchmark dose calculation, parameter estimates and estimated values are shown below in
Table 2-32.
Table 2-31 Summary of BMD Modeling Results for Seminal Vesicle Absolute Weight in
Rats Exposed to 1-BP by Inhalation
Model"
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Hill
^ 0.429
-47.533
97.3 k.
38.4
The Hill model was selected
based on the lowest AIC
because the models with
adequate goodness of fit />-valuc
and adequate fit by visual
inspection (including Hill and
Exponetial M2 - M5, excluding
Power, Polynomial and Linear)
had BMDLs < 4-fold apart
considered sufficiently close.
Exponential (M4)
Exponential (M5)b
0.337
-47.235
112
58.4
Exponential (M2)
Exponential (M3)°
0.159
-46.484
219
152
Power"1
Polynomial 3oe
Polynomial 2of
Linear
0.0576
-44.450
299
222
a Constant variance case presented (BMDS Test 2 />-value = 0.653), selected model in bold; scaled residuals for selected model
for doses 0,200,400, and 800 ppm were 0.07, -0.43, 0.61, -0.24, respectively.
b For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
c For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M2)
model.
d For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
e For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
f For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
-------
355
356
357
358
359
360
361
362
363
364
Comparisons of model fits obtained are provided in Table 2-34. The best fitting model
(Exponential (M2) with homogeneous variance because the BMDS Test 2 /;-value was 0.144)
was selected based on Akaike information criterion (AIC; lower values indicates a better fit),
chi-square goodness of fit p-value (higher value indicates a better fit) and visual inspection. The
best-fitting model is indicated in bold. For the best fitting model a plot of the model is shown in
Figure 2-15. The model version number, model form, benchmark dose calculation, parameter
estimates and estimated values are shown below in Table 2-35.
Table 2-34 Summary of BMD Modeling Results for Sperm Morphology in the Fo
Generation Exposed to 1-BP by Inhalation
Model3
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Exponential (M2)
Exponential (M3)b
0.787
-401.21
472
327
The Exponential (M2) model
was selected based on the lowest
AIC from this set of models
which have adequate /7-values,
adequate fit by visual inspection
and the BMDLs are < 1.5-fold
apart considered sufficiently
close.
Power0
Polynomial 3od
Polynomial 2oe
Linear
0.780
-401.19
473
331
Exponential (M4)
0.534
-399.30
459
230
Hill
N/Af
-397.69
482
124
Exponential (M5)
N/Af
-397.69
1 463
112
a Constant variance case presented (BMDS Test 2 />-value = 0.144), selected model in bold; scaled residuals for selected model
for doses 0,100,250, and 500 ppm were -0.05, 0.39, -0.53, 0.19, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). Hie models in this row reduced to the Exponential (M2)
model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f No available degrees of freedom to calculate a goodness of fit value.
-------
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
The BMD modeling results for sperm motility with non-homogeneous variance (BMDS test 2p-
value = 0.0001749) are summarized in Table 2-37. Although the means are sufficiently fit for
some models (e.g. the Polynomial 2° model has/rvalue of 0.516) the variances are not well
modeled BMDS Test 3 p-value = 0.0426. This result suggests that due to the poor variance
modeling for the data it is not reasonable to use BMDS for this endpoint. Instead the NOAEL of
250 ppm was used.
Table 2-37 Summary of BMD Modeling Results for Sperm Motility Fo Male Rats Following
Inhalation Exposure to 1-BP
Model"
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Polynomial 2°
0.516
657.83
386
346
Due to unacceptable fitting of
the variances no model was
selected.
Power
0.334
659.73
399
313
Polynomial 3°
0.330
659.76
397
315
Exponential (M3)
0.324
659.80
402
317
Hill
0.139
661.73
400
323
Polynomial 4°
0.137
661.76
397
314
Exponential (M5)
0.133
661.80
402
317
Linear
0.00132
671.22
237
192
Exponential (M2)
Exponential (M4)b
2.10E-04
675.10
226
178
a Modeled variance case presented (BMDS Test 2 />-value = 1.75E-04, BMDS Test 3 />-value = 0.0426), no model was selected
as a best-fitting model.
b For the Exponential (M4) model, the estimate of c was 0 (boundary). The models in this row reduced to the Exponential (M2)
model.
To investigate the effect of the poor modeling of the variances on the BMDL the observed
standard deviations were considered and the standard deviation at the highest dose is much larger
than at the other dose groups. The data set was investigated with the highest dose dropped. The
model fits with non-homogeneous variance (BMDS test 2 p-value = 0.0966) are summarized in
Table 2-38. Although the means are sufficiently fit for some models (e.g. the Polynomial 2°
model has/rvalue of 0.676) the variances are not well modeled BMDS Test 3 /rvalue = 0.0426.
-------
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
Table 2-39 Left Cauda Epididymis Absolute Weight Data Selected for Dose-Response
Modeling for 1-BP
Dose (ppm)
Number of animals
Left Cauda Epididymis Weight (mg)
Standard Deviation
0
25
0.3252
0.03673
100
25
0.3242
0.03149
250
25
0.3050
0.03556
500
23
0.2877
0.03170
750
22
0.2401
0.03529
The BMD modeling results for left cauda epididymis absolute weight with homogeneous
variance (BMDS test 2/rvalue =0.911) are summarized in Table 2-40. The best fitting model
(Polynomial 4°) was selected based on Akaike information criterion (AIC; lower values indicates
a better fit), chi-square goodness of fit p-value (higher value indicates a better fit) and visual
inspection. The Polynomial 4° model had an acceptable BMD to BMDL ratio of 1.4 and is
indicated in bold. For the best fitting model a plot of the model is shown in Figure 2-16. The
model version number, model form, benchmark dose calculation, parameter estimates and
estimated values are shown below in Table 2-41.
Table 2-40 Summary of BMD Modeling Results for Left Cauda Epididymis Absolute
Weight Fo Male R
.ats Following Inhalation Exposure to 1-BP
Model3
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Polynomial 4°
0.622 ^
-714.88
438 ^
313
The Polynomial 4° model was
selected based on the lowest
AIC from this set of models
which have adequate /7-values
(excluding Exponential M2 and
M4), adequate fit by visual
inspection and the BMDLs are
< 1.5-fold apart considered
sufficiently close.
Polynomial 3°
0.565 |
-714.69
440
316
Polynomial 2°
0.47
-714.32
437
315
Power
0.430
-714.14
444
317
Exponential (M3)
0.382
-713.91
446
320
Linear
0.133
-712.23
307
256
Hill
0.193
-712.14
444
317
Exponential (M5)
0.166
-711.91
446
320
Exponential (M2)
0.0636
-710.55
289
236
Exponential (M4)
0.0636
-710.55
289
235
a Constant variance case presented (BMDS Test 2 />-value = 0.911), selected model in bold; scaled residuals for selected model
for doses 0,100,250, 500, and 750 ppm were -0.21, 0.64, -0.65, 0.26, -0.04, respectively.
-------
435
436
437
438
439
440
441
442
443
444
445
446
447
448
Table 2-42 Right Cauda Epididymis Absolute Weight Data Selected for Dose-Response
Modeling for 1-1
:p
Dose (ppm)
Number of animals
Left Cauda Epididymis Weight (mg)
Standard Deviation
0
25
0.3327
0.03631
100
25
0.3311
0.04453
250
25
0.3053
0.04188
500
23
0.2912
0.05206
750
22
0.2405
0.04804
The BMD modeling results for right cauda epididymis absolute weight with homogeneous
variance (BMDS test 2/rvalue =0.455) are summarized in Table 2-43. The best fitting model
(Polynomial 4°) was selected based on Akaike information criterion (AIC; lower values indicates
a better fit), chi-square goodness of fit p-value (higher value indicates a better fit) and visual
inspection. The Polynomial 4° model had an acceptable BMD to BMDL ratio of 1.4 and is
indicated in bold. For the best fitting model a plot of the model is shown in Figure 2-17. The
model version number, model form, benchmark dose calculation, parameter estimates and
estimated values are shown below in Table 2-44.
Table 2-43 Summary of BMD Modeling Results for Right Cauda Epididymis Absolute
Weight Fo Male R
.ats Following Inhalation Exposure to 1-BP
Model"
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Polynomial 4°
0.493
-646.60
4851
338
The Polynomial 4° model was
selected based on the lowest
AIC from this set of models
which have adequate /7-values,
adequate fit by visual inspection
and the BMDLs are < 1.5-fold
apart considered sufficiently
close.
Polynomial 3°
0.442
-646.38
480
334
Linear
0.296
-646.32
371
303
Polynomial 2°
0.376
-646.06
472
327
Power
0.340
-645.86
474
323
Exponential (M3)
0.304
-645.63
473
317
Exponential (M2)
0.196
-645.33
350
277
Exponential (M4)
0.196
-645.33
350
270
Hill
0.142
-643.85
474
323
Exponential (M5)
0.123
-643.63
473
317
a Constant variance case presented (BMDS Test 2 />-value = 0.455), selected model in bold; scaled residuals for selected model
for doses 0,100,250, 500, and 750 ppm were -0.09, 0.63, -0.9, 0.44, -0.08, respectively.
-------
461
462
463
464
465
466
467
468
469
470
471
Table 2-45 Estrus Cycle Length Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
Number of animals
Estrus cycle Length (days)
Standard Deviation
0
25
4.2
0.49
100
25
4.5
1.05
250
25
4.7
0.9
500
23
5.5
2.17
750
22
5.6
1.79
The BMD modeling results for estrus cycle length with non-homogeneous variance (BMDS test
2 /;-value = <0.0001) are summarized in Table 2-46. The means are not adequately fit for any of
the models as shown by the goodness of fit where the model with the highest p-value is 0.0065 for
the Exponential M4 and M5 models (excluding the Hill model because a BMDL could not be
calculated). This result suggests that due to the poor model fit to the data it is not reasonable to
use BMDS for this endpoint. Instead the NOAEL of 250 ppm was used.
Table 2-46 Summary of BMD Modeling Results for Estrus Cycle Length Fo Female Rats
Following Inhalat
ion Exposure to 1-BP
Model3
Goodness of fit
BMDisd
(ppm)
BMDLisd
(ppm)
Basis for model selection
/>-value
AIC
Hill
0.00656
160.04
145
errorb
Due to inadequate fit of the
models to the data means
(shown by the goodness of fit p-
value) no model was selected.
Exponential (M4)
Exponential (M5)°
0.00650
160.05
157
79.5
Power"1
Polynomial 4°e
Polynomial 3of
Polynomial 2°g
Linear
0.00169
163.13
300 L
205
Exponential (M2)
Exponential (M3)h
7.68E-04
164.81
344
244
a Modeled variance case presented (BMDS Test 2 />-value = <0.0001, BMDS Test 3 />-value = 0.506), no model was selected as
a best-fitting model.
b BMD or BMDL computation failed for this model.
c For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
d For the Power model, the power parameter estimate was 1. Hie models in this row reduced to the Linear model.
e For the Polynomial 4° model, the b4 and b3 coefficient estimates were 0 (boundary of parameters space). The models in this
row reduced to the Polynomial 2° model. For the Polynomial 4° model, the b4, b3, and b2 coefficient estimates were 0
(boundary of parameters space). The models in this row reduced to the Linear model.
f For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
g For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
h For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M2)
model.
-------
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
of males that did not sire a litter which is equal to the number of nongravid females. The
incidence was used for modeling as a dichotomous endpoint.
Table 2-49 Fertility Index Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
Number of animals
Fertility Index (%)
Number Nongravid Females =
Males that did not Sire a Litter
0
25
92
2
100
25
100
0
250
25
88
3
500
23
52
12
750
22
0
25
The BMD modeling results for the fertility index are summarized in Table 2-50. The best fitting
models were the LogLogistic and Dichotomous-Hill based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. Dichotomous-Hill model slope parameter was at the
boundary value of 18 which indicates some concern for using this model fit and so instead the
LogLogistic model selected. The LogLogistic and Dichotomous-Hill models had nearly the same
BMDLs with LogLogistic slightly lower (356 ppm) than Dichotomous-Hill (363 ppm). For the
best fitting model a plot of the model is shown in Figure 2-18. The model version number, model
form, benchmark dose calculation, parameter estimates and estimated values are shown below in
Table 2-51.
Table 2-50 Summary of BMD Modeling Results for Fertility Index of Fo Rats Following
Model"
Goodness of fit
BMDioPct
(ppm)
BMDLioPct
(ppm)
Basis for model selection
/>-value
AIC
LogLogistic
0.388
75.396
448
356
The LogLogistic model was
selected based on the lowest
AIC from this set of models
which have adequate goodness
of fit /7-value (excluding
Quantal-Linear, Multistage 2°,
Probit and Logistic) and
adequate fit by visual inspection
and the BMDLs are < 2-fold
apart considered sufficiently
close. The Dichotomous-Hill
model had concern for the fit
based on the slope parameter at
the boundary and so instead the
LogLogistic was selected.
Dichoto mous -Hill
0.388
75.396
448
363
Multistage 4°
0.355
75.682
306
219
Weibull
0.253
77.024
361
252
Gamma
0.256
77.045
361
260
LogProbit
0.223
77.357
461
352
Multistage 3°
0.161
78.153
250
202
Logistic
0.0103
80.981
238
182
Probit
0.0031
82.358
208
159
Multistage 2°
0.0152
85.979
173
143
Quantal-Linear
0
106.73
68.4
52.1
-------
527
528
529
530
531
532
533
534
535
536
The BMD modeling results for the number of implantations sites are summarized in Table 2-53.
The best fitting models were the Linear and Power based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit /;-value (higher value indicates a
better fit) and visual inspection. Based on the parameter estimate for the Power model it reduced
to the Linear, so the Linear model was selected. For the best fitting model a plot of the model is
shown in Figure 2-19. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below in Table 2-54.
Table 2-53 Summary of BMD Modeling Results for Implantations Sites in Fo Rats
Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
BMDisd
(ppm) P
BMDLisd
(ppm)
Basis for model
selection
/>-value
AIC
Linear
Powerb
0.936
284.66
80.8
56.1
282
188
Linear and Power
models were selected
based on the lowest AIC
from this set of models
which have adequate p-
values, adequate fit by
visual inspection and
the BMDLs are < 1.5-
fold apart considered
sufficiently close.
Exponential (M2)
0.901
284.74
74.1
48.1
270
166
Exponential (M4)
0.901
284.74
74.1
37.3
270
138
Polynomial 3°
0.741
286.64
85.5
56.2
295
188
Polynomial 2°
0.724
286.66
^84.3
56.1
289
188
Hill
0.715
286.67
80.6
55.8
282
195
Exponential (M3)
0.669
286.71
82.3
48.2
278
167
Exponential (M5)
N/A°
288.71
82.3
48.2
278
167
a Modeled variance case presented (BMDS Test 2 />-value = 0.0493), selected model in bold; scaled residuals for selected
model for doses 0,100,250, and 500 ppm were -0.17, -0.23,1, -1, respectively.
b For the Power model, the power parameter estimate was 1. Hie models in this row reduced to the Linear model.
c No available degrees of freedom to calculate a goodness of fit value.
-------
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
lalpha
12.2915
2.51459
rho
-3.77194
0
betaO
15.393
15.7286
betal
-0.00952791
-0.01237
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
23
15.3
15.4
2.53
2.69
-0.166
100
25
14.3
14.4
3.09
3.03
-0.231
250
22
13.8
13
4.23
3.69
1
500
11
9
10.6
4.54
5.41
-0.999
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
-140.289933
5
290.579865
A2
-136.366566
8 1
288.733132
A3
-138.26616
6
288.532319
fitted
-138.332408
4
284.664816
R
-151.740933
2
307.481866
Tests of Interest
Test
-2*log(Likelihood Ratio)
Test df
/>-value
Test 1
30.7487
6
<0.0001
Test 2
7.84673
3
0.04929
Test 3
3.79919
2
0.1496
Test 4
0.132497
2
0.9359
544
-------
584 2-57. Also a plot of the Hill model is shown in Figure 2-21 and the model version number, model
585 form, benchmark dose calculation, parameter estimates and estimated values are shown below in
586 Table 2-57.
587
588 Table 2-56 Summary of BMD Modeling Results for Body Weight of Fi Male Rat Pups on
589 PND 28 Following Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation
Model"
Goodness of fit
BMD
1SD
(ppm)
BMDL
1SD
(ppm)
BMD
5RD
(ppm)
BMDL
5RD
(ppm)
Basis for model selection
/>-value
AIC
Exponential
(M2)
Exponential
(M3)b
0.449
411.46
334.07
228.77
174
123
The Exponential (M2) model was
selected based on the lowest AIC
from this set of models which have
adequate /7-values and adequate fit
by visual inspection. The Hill model
has the lowest BMDL and the BMDL
is > 5-fold apart from other model
BMDLs not considered sufficiently
close, however the BMDL is > 4-fold
from the lowest dose and BMD /
BMDL ratio is 4-fold and the
Exponential (M2) model is in line
with the result from pup body weight
decreases observed in this study at
other time points.
Power0
Polynomial 3od
Polynomial 2oe
Linear
0.406
411.66
345.22
242.64
a 183
133
Hill
0.578
412.17
234.74
85.21
92.2
23.2
Exponential
(M4)
Exponential
(M5)f
0.512
412.29
238.92
95.80
101
36.8
a Constant variance case presented (BMDS Test 2 />-value = 0.785), selected model in bold; scaled residuals for selected model
for doses 0,100,250, and 500 ppm were 0.77, -0.88, -0.17, 0.44, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). Hie models in this row reduced to the Exponential (M2)
model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
591
-------
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
lnalpha
4.19824
4.17769
rho
n/a
0
a
86.7871
78.9392
b
0.000295534
0.000288601
c
n/a
0
d
n/a
1
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
23
88.1
86.79
7.6
8.16
0.7717
100
24
82.8
84.26
7.74
8.16
-0.8765
250
21
80.3
80.61
9.04 1
8.16
-0.1719
500
10
76
74.87
9.45
8.16
0.4398
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
i -201.9297
5
413.8595
A2
-201.395
8
418.7901
A3
-201.9297
5
413.8595
R
-210.4356
2
424.8712
2
-202.7313
3
411.4626
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1
18.08
6
0.006033
Test 2
1.069
3
0.7845
Test 3
1.069
3
0.7845
Test 4
1.603
2
0.4486
599
-------
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
23
88.1
88
7.6
8.09
0.0793
100
24
82.8
83.3
7.74
8.09
-0.299
250
21
80.3
79.6
9.04
8.09
0.398
500
10
76
76.6
9.45
8.09
-0.235
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
-201.929732
5
413.859464
A2
-201.39503
8
418.790061
A3
-201.929732
5
413.859464
fitted
-202.084541
4
412.169082
R
-210.435607
2
424.871213
Tests of Interest
Test
2*log(Likelihood
Ratio)
Test df
p-value
Test 1
18.0812
6
0.006033
Test 2
1.0694
3
0.7845
Test 3
1.0694
3
0.7845
Test 4
0.309618
1
0.5779
606
-------
623
624
625
To investigate the effect of the poor modeling of the variances on the BMDL, the models were
run using the smallest dose standard deviation (2.29), highest (3.87) and pooled (2.89) for all dose
levels and the modeling results are summarized in Table 2-61.
-------
626
627
Table 2-61 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following Inhalation Exposure of
Model3
Smallest Standard Deviation
Pooled Standard Deviation
Largest Standard Deviation
Ratio
BMDLs
Smallest
to Largest
Std Dev
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
/>-value
AIC
/j-valuc
AIC
/>-value
AIC
Polynomial 3°
0.518
186.54
360
274
0.661
218.16
360
183
0.793
258.09
360
145
1.9
Polynomial 2°
0.318
187.51
304
199
0.485
218.78
304
260
0.667
258.44
304
140
1.4
Power
0.331
188.16
465
247
0.441
219.93 1
465
200
0.564
259.96
460
148
1.7
Exponential
(M3)
0.331
188.16
473
249
0.441
219.93
470
202
0.564
259.96
473
143
1.7
Hill
N/Ab
190.16
466
248
N/Ab
221.93
465
200
N/Ab
261.96
442
138
1.8
Exponential
(M5)
N/Ab
190.16
470
249
N/Ab
221.93
470
202
N/Ab
261.96
473
139
1.8
Linear
0.0533
191.08
193
146
0.154
221.07
193
138
0.348
259.74
193
127
1.1
Exponential
(M2)
0.0443
191.45
188
139
0.137
221.31
188
131
0.325
259.88
188
119
1.2
Exponential
(M4)
0.0443
191.45
188
131
0.137
221.31
188
115
0.325
259.88
188
90.2
1.5
a Constant variance case presented (BMDS Test 2 />-value = 1BMDS Test 3 />-value = 1.), no model was selected as a best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
628
-------
678
679 Table 2-67 BMD Modeling Results for Pup Body Weight in Rats Exposed to 1-BP Via
680 Inhalation in ppm BMR = 5% Relative Deviation.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose + beta_2*doseA2 + ...
A constant variance model is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 287.938
BMDL at the 95% confidence level = 135.688
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
alpha
10.1836
10.5942
rho
n/a
0
betaO
28.9615
28.8658
betal
0
0
beta_2
-0.000017466
-0.000019675
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
22
29.2
29^
2.77
3.19
0.35
100
17
28.1
28.8
2.43
3.19
-0.887
250
15
28.4
27.9
3.65
3.19
0.643
500
16
24.5
24.6
4.14
3.19
-0.119
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
-115.551371
5
241.102743
A2
-112.600048
8
241.200097
A3
-115.551371
5
241.102743
fitted
-116.227119
3
238.454239
R
-125.255153
2
254.510306
-------
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 154.623
BMDL at the 95% confidence level = 116.114
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
alpha
30.4578
30.9275
rho
n/a
0
betaO
49.5516
49.615
betal
-0.0160234
-0.0160705
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
22
49.5
49.6
5.14
5.52
-0.0439
100
17
46.9
47.9
5.03
5.52
-0.784
250
15
47.6
45.5
1 54
5.52
1.44
500
16
40.8
41.5
6.7
5.52
-0.536
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
^-153.048201
5
316.096402 >
A2
-152.146228
8
320.292456
A3
-153.048201
316.096402
fitted
-154.572024
3
315.144048
R
-163.858303
2
331.716606
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1
23.4241
6
0.0006662
Test 2
1.80395
3
0.6141
Test 3
1.80395
3
0.6141
Test 4
3.04765
2
0.2179
705
-------
743
744
745
746
747
748
749
750
751
752
753
754
The BMD modeling results for decreased brain weight in Fo males with non-homogeneous
variance (BMDS test 2/rvalue = 0.000386) are summarized in Table 2-75. Although the
variances are non-homogeneous and not well modeled for any of the non-homogeneous variance
models the means were well-modeled (the highest /;-value is 0.618 for the Exponential (M4)
model with non-homogeneous variances).
Table 2-75 Summary of BMD Modeling Results for Brain Weight of Fo Males Following
Inhalation Exposure to 1-BP
Model3
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
/>-value
AIC
Exponential (M4)
0.618
-408.61
372
159
Hill
0.340
-406.66
354
107
Exponential (M5)
0.152
-405.52
115
102
Exponential (M2)
Exponential (M3)b
0.0868
-405.00
636
453
Power0
Polynomial 4od
Polynomial 2oe
Linearf
0.0804
-404.83
644
463
Polynomial 3og
0.0804
-404.83
644
463
a Modeled variance case presented (BMDS Test 2p-value = 3.86E-04, BMDS Test 3 p-value = 5.66E-04), no model was selected as a best-
fitting model.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
11 For the Polynomial 4° model, the b4 and b3 coefficient estimates were 0 (boundary of parameters space). The models in this row reduced to
the Polynomial 2° model. For the Polynomial 4° model, the b4, b3, and b2 coefficient estimates were 0 (boundary of parameters space). The
models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row reduced to the Linear
model.
f The Linear model may appear equivalent to the Polynomial 3° model, however differences exist in digits not displayed in the table.
s The Polynomial 3° model may appear equivalent to the Power model, however differences exist in digits not displayed in the table. This also
applies to the Polynomial 4° model. This also applies to the Polynomial 2° model. This also applies to the Linear model.
To investigate the effect of the poor modeling of the variances on the BMDL, the models were
run using the smallest dose standard deviation (0.091), highest (0.177) and the pooled (0.0907) for
all dose levels and the modeling results are summarized in Table 2-76.
-------
755
756
Table 2-76 BMD Modeling Results for Brain Weight of Fo Male Rats Following Inhalation Exposure to 1-BP in a Two-
Model3
Smallest Standard Deviation
Pooled Standard Deviation
Largest Standard Deviation
Ratio
BMDLs
Smallest
to Largest
Std Dev
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
Goodness of fit
BMDsrd
(ppm)
BMDLsrd
(ppm)
/>-value
AIC
/>-value
AIC
/j-valuc
AIC
Exponential
(M4)
0.0893
-477.73
375
164
0.108
-467.70
375
159
0.553
-303.82
375
78.7
2.1
Hill
0.0423
-476.44
289
106
0.0513
-466.35
1 289
106
0.315
-302.00
289
70.4
1.5
Exponential
(M5)
0.0398
-476.34
246
104
0.0484
-466.26
246
103
0.309
-301.97
246
82.4
1.3
Exponential
(M2)
0.0238
-475.11
669
515
0.0332
-465.43
669
510
0.503
-304.65
669
420
1.2
Exponential
(M3)
0.0238
-475.11
669
515
0.0332
-465.43
669
510
0.503
-304.65
669
420
1.2
Power
0.0223
-474.96
674
523
0.0312
-465.29
674
518
0.496
-304.62
674
430
1.2
Polynomial 4°
0.0223
-474.96
674
^523
0.0312
-465.29
674
518
0.496
-304.62
674
430
1.2
Polynomial 2°
0.0223
-474.96
674
523
0.0312
-465.29
674
518
0.496
-304.62
674
430
1.2
Linear
0.0223
-474.96
674
523
0.0312
-465.29
674
518
0.496
-304.62
674
430
1.2
Polynomial 3°
0.0223
-474.96
674
523
0.0312
-465.29
674
518
0.496
-304.62
674
430
1.2
757
758
a Constant variance case presented (BMDS Test 2 />-value = 1BMDS Test 3 />-value = 1.), no model was selected as a best-fitting model.
-------
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
lnalpha
-0.0282712
-1.99881
rho
-15.3239
-8.92906
a
1.40066
1.33604
b
0.000120467
0.000129477
c
n/a
0
d
n/a
1
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
22
1.4
1.4
0.06
0.07
-0.3121
100
17
1.39
1.38
0.09
0.08
0.3231
250
15
1.37
1.36
0.12
0.09
0.3377
500
15
1.31
1.32
0.1
0.12
-0.3236
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
131.2578
5
-252.5155
A2
134.8828
8
-253.7656
A3
133.1137
6
| -254.2275
R
126.819
2
-249.638
2
132.6574
4
-257.3148
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1
16.13
6
0.01309
Test 2
7.25
3
0.06434
Test 3
3.538
2
0.1705
Test 4
0.9127
2
0.6336
827
-------
Table of Data and Estimated Values o
'Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
22
1.47
1.46
0.08
0.08
0.989
100
17
1.43
1.46
0.08
0.08
-1.62
250
15
1.47
1.46
0.06
0.08
0.522
500
16
1.36
1.36
0.1
0.08
-0.00000182
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
144.826466
5
-279.652932
A2
146.516124
8
-277.032248
A3
144.826466
5
-279.652932
fitted
142.841294
3
-279.682588
R
135.116612
2
-266.233223
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1
22.799
6 \
0.0008667
Test 2
3.37932
3
0.3368
Test 3
3.37932
3
0.3368
Test 4
3.97034
2
0.1374
-------
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
lnalpha
-0.107405
0.415293
rho
1.46448
1.29675
a
26.8244
26.46
b
0.0174245
0.00510395
c
0.172048
0.15837
d
n/a
1
Table of Data and Estimated Values of Interest
Dose
N
Obs Mean
Est Mean
Obs Std Dev
Est Std Dev
Scaled Resid
0
5
25.2
26.82
15.25
10.54
-0.3447
10
5
23.8
23.27
7.53
9.5
0.1241
50
5
15.2
13.91
5.54
6.51
0.4434
200
5
5.2
5.3
3.42
3.21
-0.0668
1000
5
4.4
4.62
3.65
2.9
-0.1656
Likelihoods of Interest
Model
Log(likelihood)
# Param's
AIC
A1
-62.64066
6
137.2813
A2
-54.60856
10
129.2171
A3
-56.01777
7
126.0355
R
-73.64274
2
151.2855
4
-56.06343
5
122.1269
Tests of Interest
Test
-2*log(Likelihood
Ratio)
Test df
/j-valuc
Test 1
38.07
8
<0.0001
Test 2
16.06
4
0.002934
Test 3
2.818
3
0.4205
Test 6a
0.09133
2
0.9554
878
879
------- |