# NOT RUN {
##obtain the plot of the summary for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
DLEweights=c(3,3),
DLEdose=c(25,300),
data=data)
##The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
nu=c(a=1,b=0.025),data=data,c=0)
##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
DLEEndOfTrialtarget=0.3)
##The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (for illustration)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with a dose of 25
##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
model=DLEmodel,
Effmodel=Effmodel,
stopping=myStopping,
increments=myIncrements,
cohortSize=mySize,
data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}
myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}
## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1).
mySim <-simulate(object=design,
args=NULL,
trueDLE=myTruthDLE,
trueEff=myTruthEff,
trueNu=1/0.025,
nsim=1,
## this would need to be increased in the real
## application:
mcmcOptions=McmcOptions(burnin=10, step=1, samples=50),
seed=819,
parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
trueDLE=myTruthDLE,
trueEff=myTruthEff)
##Then plot the summary of the simulations
print(plot(MYSUM))
##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
DLEEndOfTrialtarget=0.3,
TDderive=function(TDsamples){
quantile(TDsamples,prob=0.3)},
Gstarderive=function(Gstarsamples){
quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
cohortSize=mySize,
startingDose=25,
model=DLEmodel,
Effmodel=Effmodel,
data=data,
stopping=myStopping,
increments=myIncrements)
##options for MCMC
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1).
# mySim<-simulate(design,
# args=NULL,
# trueDLE=myTruthDLE,
# trueEff=myTruthEff,
# trueNu=1/0.025,
# nsim=1,
# mcmcOptions=options,
# seed=819,
# parallel=FALSE)
#
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
# trueDLE=myTruthDLE,
# trueEff=myTruthEff)
#
# ##Then plot the summary of the simulations
# print(plot(MYSUM))
##OR if the 'EffFlexi' class is used
## for the efficacy model
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
##Specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
cohortSize=mySize,
startingDose=25,
model=DLEmodel,
Effmodel=Effmodel,
data=data,
stopping=myStopping,
increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}
myTruthEff<- c(-0.5478867, 0.1645417, 0.5248031, 0.7604467,
0.9333009 ,1.0687031, 1.1793942 , 1.2726408 ,
1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}
##The simulations
# ##For illustration purpose only 1 simulation is produced (nsim=1).
# mySim<-simulate(object=design,
# args=NULL,
# trueDLE=myTruthDLE,
# trueEff=myTruthEff,
# trueSigma2=0.025,
# trueSigma2betaW=1,
# nsim=1,
# mcmcOptions=options,
# seed=819,
# parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
# trueDLE=myTruthDLE,
# trueEff=myTruthEff)
#
# ##Then plot the summary of the simulations
# print(plot(MYSUM))
# }
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