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1
Comparing Cancer Risks betweenComparing Cancer Risks between
Radiation and Dioxin ExposureRadiation and Dioxin Exposure
Based on Two-Stage ModelBased on Two-Stage Model
Tsuyoshi Nakamura
Faculty of Environmental Studies,
Nagasaki University
David G. Hoel
Dept. of Biometry and Epidemiology,Medical University of South Carolina
1. Two-Stage Model2. Historical Aspects3. Estimation Method4. Radiation by JANUS5. Dioxin by KocibaSummary Conclusion
2
NormalCellsIntermediate
CellsMalignantCells
É 1
É 2
ɬɿ
Three States of CellsNormal, Intermediate and Malignant
Four Parameters for Rates
1 : First Mutation Rate for NI
: Clonal Expansion Rate for I
: Death Rate for I
2 : Second Mutation Rate for I
M
N I M
Two-Stage Model
3
HistoryMathematical tool based on Molecula
r biology to study Mechanistic processes in Cancer development
(Moolgavkar, Venzon, Knudson, 70’s)
Special FeatureExplicit modeling of
Clonal expansion, Differentiation and Mutation of I-cells
as a Continuous Stochastic Process
Cancer Incidence Data(time, type, covariate)
(t, 1, x): endpoint
(t, 0, x): censored
4
UnidentifiabilityAll parameters are not identifiable. Re
parameterization or Assumptionis necessary.
Non-ConvergenceMLE of the identifiable parameters
are still often hardly obtainable,because of the peculiar shape of the li
kelihood surface (Portier et at. 1997).
ProblemsProblems
Non-Standard Algorithm
Lack of Confidence in Results
Lack of Comparison among Studies
5
Survivor function S(t) Probability of No Malignant Cell at t,
is obtained by solving a series of differential equations,
derived from Stochastic processes on Probability Generating Function
(Moolgavkar et al 1990; Kopp et al 1994; Portier et al 1996;)
Stochastic processes
M(t)=(x(t),y(t),z(t)) denote the number of
N-, I- and M-cells at t, respectively.
M(t): Continuous Markov Birth-Death process
S(t)= i,jProb{M(t)=(i,j,0)}
6
Differential Equations
It follows that (Portier et al 1996)
dG(t)/dt= 1G(t)H(t)-1G (t)
dH(t)/dt= H(t)2+-(++2)H(t)
G(0)=1, H(0)=1
Probability Generating Function
P(i,j,k|t)=Prob{M(t)=(i,j,k) | M(0)=(1,0,0) }
G(u,v,w|t)= i,j,k P(i,j,k|t)uivjwk
and
Q(i,j,k|t)=Prob{M(t)=(i,j,k) | M(0)=(0,1,0) }
H(u,v,w|t)= i,j,k Q(i,j,k|t)uivjwk
S(t)=i,jP(i,j,0|t)=G(1,1,0|t)
7
Survivor Function S(t) X0 = Number of N-cells, Large and Constant = NI Rate per Cell per unit Time==> 1=X0
S(t)=exp{-(t)},
(t){t(R++log[{R-+(R+)e-Rt}/2R]} is Cumulative Hazard with new paramete
rs =1/, =-2 and R2=(--2)2+42 Original likelihood
: Net Proliferation Rate
=12=(R2-2)/4:Overall Mutation Rate
l(,,) based on (t) is termed
Original likelihood.
Non-convergence is frequent !
8
1, and
2 are employed to emphasize
these parameters are valid only when =0
l(1,,
2 ) based on (t|=0) is termed
Conditional likelihood.
Looks Better Shape!
Conditional likelihood
*1
β* *t+ log μ*2 +β*exp{−(β* +μ*
2)t}β*+μ*
2
⎧
⎨ ⎪
⎩ ⎪
⎫
⎬ ⎪
⎭ ⎪
(t|=0)= [ ]
Put =0 then
9
TransformationTransformationConditional likelihood converges better!
Biological interpretation of parameters is ? It ignores the death of the I-cells.
Biological parameters estimated by
1, and 2 are
=1, =-
2 and =1
2
(Nakamura and Hoel 2002)
Thus, MLE of , and are obtained
from Conditional Likelihood !
Practically=, since 2 is small
10
Conditional vs Original
Comparison on Experimental Data
JANUS data for Radiation Risk study
On and Neutron in Mice
Argonne National Laboratory (1953-1970 )
Reliable Pathological information
Kociba data for Chronic Toxicity study
on TCDD in Rats
Dow Chemical (1978)
Reliable Pathological information
11
Illustration of Two-Stage Model
Cited from Moolgavkar(1999)
Statitics for the Environment 4, Wiley
1X0
2
12
ControlControl mice mice 3707 with 1894 Cancer
Original likelihood: l = -13692.7, ||U||<0.001
Parameter Estimate SE log 0.05632 0.16436log-4.8185 0.04563
log -17.660 0.2086Initial Trial Values are assigned as
=/, = and =
1
Conditional Likelihood: l =-13692.7, ||U||< 0.001
Parameter Estimate SE log
1 -4.7618 0.10524 log-4.8182 0.03767log
2 -12.8980.13539 log -17.660 0.1760 log 0.05632 0.1244
13
Regression Modellog=a+bDose
(Contol + ) 7402 mice with 4133 Cancer
ConditionalLikelihood: l=-29446.65, ||U||=0.002
Const.a (SE) Slope b (SE)log
-4.931 (0.0817) 0.00717 0.00115)log -4.851 (0.0278) -0.000345 (0.000071) log
2 -12.43 (0.1014) -0.002934 (0.00112
6)log-17.37 (0.1181) 0.00424 (0.000241)--------------------------------------------------------------------------------------------------------------------------------------------------
Original likelihood: l=-29446.69, ||U||=0.2542
Const.a (SE) Slope b (SE)log-0.0797 (0.1318) 0.00749 (0.00131)
log -4.852 (0.0373) 0.000345 (0.000077)log-17.37 (0.1536) 0.00424 (0.000266)
All Estimates are of p<0.01
14
=12=X02
2) X0 is Constant not affected by Exposure
3) Effect of exposure on and that on 2
are the same ( Moolgavkar et al ,1999),
4) log=a+bDose
==> Dose effect on and that on 2 is b/2
Effect of Exposure on
Mutation and Promotion
Dose Effect on Mutation Rate
and Net proliferation Rate
may be obtained from
Conditional likelihood
without Additional Assumption!
15-8
-6
-4
-2
0
2
-8 -6 -4 -2 0 2
Log Cumulative HazardsLog Cumulative Hazards
Dose 0 : Subjects 3707 Cancer 1894
Two-Stage (H) vs K-M(V)
V
H
16
Dose 86 : Subjects 1376 Cancer 960
Log Cumulative HazardsLog Cumulative HazardsTwo-Stage (H) vs K-M(V)
-8
-6
-4
-2
0
2
-8 -6 -4 -2 0 2
V
H
17
Dose 756 : Subjects 396 Cancer 190
Log Cumulative HazardsLog Cumulative HazardsTwo-Stage (H) vs K-M(V)
-8
-6
-4
-2
0
2
-8 -6 -4 -2 0 2
V
H
18
Original Likelihood: l =-207.585, ||U||=5.7489
Incomplete-convergent case Const. SE Slope SElog-0.4724 0.7395 non log -3.773 0.04589 0.0631 0.0200log-27.14 0.3368 non
Regression Coefficients for Dioxin Regression Coefficients for Dioxin 205 rats,31 cancer, log=a+blog(1+Dose)
Conditional Likelihood: l = -206.77,||U||=0.0004 Const. SE Slope SElog
-3.780 0.7075 non log -3.961 0.1062 0.0680 0.01497log
2 -20.82 1.371 non log-24.60 1.259 non
Original Likelihood: l = -207.012, ||U||= 0.0012 Const.a SE Slope SElog0.0865 0.8192 non
log -3.979 0.1083 0.0658 0.01466log-24.32 1.216 non
19-14
-12
-10
-8
-6
-4
-2
0
2
150 250 350 450 550 650 750
100
10
1
0
Log Cumulative Hazards
for Dioxin Doses
week
20-15
-10
-5
0
5
0 50 100 150 200
Log Cumulative Hazards
for Radiation Doses
756
400
197.6
86.31
43.15
0
week