crmPack (version 1.0.6)

simulate,DualResponsesSamplesDesign-method: This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesSamplesDesign where DLEmodel used are of ModelTox class object and efficacy model used are of ModelEff class object (special case is EffFlexi class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process

Description

This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesSamplesDesign where DLEmodel used are of ModelTox class object and efficacy model used are of ModelEff class object (special case is EffFlexi class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process

Usage

# S4 method for DualResponsesSamplesDesign
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Value

an object of class PseudoDualSimulations or PseudoDualFlexiSimulations

Arguments

object

the DualResponsesSamplesDesign object we want to simulate the data from

nsim

the number of simulations (default :1)

seed

see setSeed

trueDLE

a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.

trueEff

a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in args.

trueNu

(not with EffFlexi) the precision, the inverse of the variance of the efficacy responses

trueSigma2

(only with EffFlexi) the true variance of the efficacy responses which must be a single positive scalar.

trueSigma2betaW

(only with EffFlexi) the true variance for the random walk model used for smoothing. This must be a single postive scalar.

args

data frame with arguments for the trueDLE and trueEff function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.

firstSeparate

enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

mcmcOptions

object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

parallel

should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Examples

##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used

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 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})


##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 (only for illustration such a low 
##sample size)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with 
##a dose of 25


##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 and efficacy curve
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}


myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##simulate the trial for 10 times involving samples
##for illustration purpose we use 10 burn-ins to generate 50 samples
options<-McmcOptions(burnin=10,step=1,samples=50)
##For illustration purpose only 1 simulations are produced (nsim=1). 
mySim<-simulate(design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 mcmcOptions=options,
                 seed=819,
                 parallel=FALSE)



##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
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
d1 <- data@doseGrid
myTruthGain <- (myTruthEff)/(1+(myTruthDLE(d1)/(1-myTruthDLE(d1))))


mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)