1 modeling and estimation of benchmark dose (bmd) for binary response data wei xiong
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Modeling and Estimation of
Benchmark Dose (BMD)
for Binary Response Data
Wei Xiong
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Outline
Benchmark dose (BMD) and datasets Statistical models
logistic probit multi–stage gamma multi–hit
Model fitting and analyses Conclusions
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where,
ADI: acceptable daily
intake
SF: safety factor
NOAELADI
SF
In environment risk assessment,
NOAEL (no-observed-adverse-effect level)
is used to derive a safe dose,
4(Filipsson et al., 2003)Problem:
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BMD: Point estimate of the dose which induces a
given response (e.g. 10%) above unexposed
controls
BMDL: 1–sided 95% confidence lower limit
for BMD
Benchmark dose (BMD)
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Benchmark dose (BMD)
• Fit a model to all data
• Estimate the BMD
from a given BMR (10%)
• Derive “safe dose”
from BMD
(Filipsson et al., 2003)
Advantage: BMD uses all the
data information by fitting a model
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Non–cancer data Ryan and Van (1981)
i di ri ni
1 24 0 30
2 27 0 30
3 30 4 30
4 34 11 30
5 37 10 30
6 40 16 30
7 45 26 30
8 50 26 30
30 mice in each dose group
drug: botulinum toxin in 10–15 gram
response: death (Y or N) within 24 hrs
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Plot of Non-cancer Data
log(dose)
Pro
pn
of
Mic
e M
ort
alit
y
3.2 3.4 3.6 3.8
0.0
0.2
0.4
0.6
0.8
Plot of non-cancer Data
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Cancer dataBryan and Shimkin (1943)
i d i r i n i
1 3.9 0 19
2 7.8 3 17
3 15.6 6 18
4 31 13 20
5 62 17 21
6 125 21 21
17 to 21 mice in each dose group
drug: carcinogenic methylcholanthrene in 10–6 gram
response: tumor (Y or N)
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Plot of cancer data
Plot of Cancer Data
log(dose)
Pro
pn
of
Mic
e B
ea
rin
g T
um
ors
2 3 4
0.0
0.2
0.4
0.6
0.8
1.0
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How to estimate BMD ?
What models to be used
? Need to use different models for the cancer
and non-cancer data
How to fit the model curve
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Statistical models
( ) (1 ) ( ; , )P d F d
• Logistic • Probit• Multi–stage• Gamma multi–hit
Model form:
where,
1> >=0 is the background response as dose0
F is the cumulative dist’n function
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Probit model
Assuming:
log(d) is approx. normally distributed
2log / 21( )
2
e d xP d e dx
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Logistic model
( log )
1( )
1 e dP d
e
Assuming:
log(d) has a logistic distribution
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1( ) (1 )[1 exp( )]
n jjj
P d d
Multi–stage model (Crump, 1981)
Assuming:
1. Ordered stages of mutation, initiation or transformation
for a cell to become a tumor
2. Probability of tumor occurrence at jth stage is
proportional to dose by jd j
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Gamma multi–hit model (Rai and Van, 1981)
1
0
1
0
( ) (1 )
d t
t
t e dtP d
t e dt
Assuming: a tumor incidence is induced by at least 1
hits of units of dose and follows a Poisson distribution
The gamma model is derived from the Poisson dist’n of
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Model fitting
Models are fit by maximum likelihood method
Model fitting tested by Pearson’s 2 statistic
If p-value 10%, the model fits the data well and
the mle of BMD is obtained from the fitted model
22 2
1
( )~
(1 )
mii i
n pi
ii i
r n P
n P P
where, is estimated from the fitted model
iP
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BMDL by LRT
(Crump and Howe, 1985)
where,
and are model parameters
P is the log(BMD) at response = p
P
2P 1 2*0.05,1
2[ ( , ) ( , )]
= ( , ) ( , )D D
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The BMDL is the value P, which is lower than the
mle , so that, P
P
21 2*0.05,1
2[ ( , ) ( , )]
=
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BMDL by Fieller’s Theorem (Morgan, 1992)
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22
221 0.05 12
11 12 22 1122
( )( )1
2 ( )(1 )
P P
P P
Vc
c V
Z VV V V c V
Vc
Fieller’s Theom constructs CI for the ratio of R.V.
For logistic model,
the BMDL is derived as,
where,
211 12 22~ (0, 2 )N V V V
2 2(1 0.05) 22 /c Z V
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BMDL computation
BMDS (benchmark dose software, US EPA)
provides the 4 models for BMDL using LRT
S–Plus calculates BMDL using LRT and Fieller’s Theorem
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BMDS logistic modeling for non–cancer data
(Pearson’s 2, p = 0.325 > 0.1)
0
0.2
0.4
0.6
0.8
1
25 30 35 40 45 50
Fra
cti
on
Aff
ec
ted
dose
Log-Logistic Model with 0.95 Confidence Level
05:48 07/02 2005
BMDL BMD
Log-Logistic
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0
0.2
0.4
0.6
0.8
1
10 20 30 40 50
Fra
cti
on
Aff
ec
ted
dose
Multistage Model with 0.95 Confidence Level
05:39 07/02 2005
BMDBMDL
Multistage
BMDS multi–stage modeling for non–cancer data
(Pearson’s 2, p = 0.0000)
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BMDS two–stage modeling for cancer data
(Pearson’s 2, p = 0.556)
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120
Fra
cti
on
Aff
ec
ted
dose
Multistage Model with 0.95 Confidence Level
07:02 07/01 2005
BMDBMDL
Multistage
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BMDL=1.536 by LRT (Probit model for cancer data)
x0 values
Pro
file
De
via
nce
1.4 1.6 1.8 2.0
01
23
45
Lo
we
r C
L=
1.5
36
13
91
51
80
69
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Software Model
Logistic Probit
BMDS 30.042 30.039
S–plus 30.042 30.039
MLE of BMD (non–cancer data)
( p–value by Pearson’s 2 )
(0.325) (0.386)
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Summary of BMDL (non–cancer data)
Methods Software Model
Logistic Probit
LRTBMDS 28.143 28.296
S–plus 28.139 28.293
Fieller’s Theorem
S–plus 27.991 28.218
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Software Model
Logistic Probit Two–stage
Multi–hit
BMDS 7.168 7.203 4.867 6.334
S–plus 7.171 7.199
MLE of BMD (cancer data)
# p–value by Pearson’s 2
# 0.585 # 0.666 # 0.556 # 0.602
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Methods SoftwareModel
Logistic Probit Two–
stage
Multi–
hit
LRTBMDS 4.434 4.647 3.087 3.290
S–plus 4.434 4.647
Fieller’s
Theorem
S–plus 4.181 4.519
Summary of BMDL (cancer data)
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Conclusions
Non–cancer data, BMD = 30.042 (logistic) and 30.039
(probit) in 10–15 gram; cancer data, BMD = 7.168
(logistic), 7.203 (probit), 4.867 (multi–stage) and 6.334
(multi–hit).
Logistic and probit model fit both data sets well, multi–
stage and multi–hit fit only the cancer data well.
BMDL obtained by Fieller’s Theorem seems to be smaller
than that by LRT, why ?
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Questions ?
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A note on qchisq( ) of 1–sided 95%
> (qnorm(1 - 0.05))^2 [1] 2.705543
> qchisq(1 - 2 * 0.05, 1) [1] 2.705543
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95% CI for proportion in slides 21 & 22
When n is large, nP 5 and n(1-P) 5, the sample
proportion p is used to infer underlying proportion P.
p is approximately normal with mean P and
s.e.=sqrt(P(1-P)/n)
Solving the following equation,
1 0.05/ 2
| | 1/(2 )
/
p P nZ
PQ n
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Fitted and re–parameterized model
log ( )1e
Pd
P
log ( )1ec
log ( ) ( )1e P
Pc d
P
Fitted logistic model
Re-parameterized logistic model
where,
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Abbott’s Formula
(1 )P c c BMR
where,
P – observed response
c – response at dose zero
BMR – benchmark response with
default value 10%