crmPack (version 1.0.6)

plot,PseudoDualFlexiSimulations,missing-method: Plot for PseudoDualFlexiSimulations

Description

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory

Summary of the trajectory of the simulated trials

dosesTried

Average proportions of the doses tested in patients

sigma2

The variance of the efficacy responses

sigma2betaW

The variance of the random walk model

You can specify one or both of these in the type argument.

Usage

# S4 method for PseudoDualFlexiSimulations,missing
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Arguments

x

the PseudoDualFlexiSimulations object we want to plot from

y

missing

type

the type of plots you want to obtain.

...

not used

Examples

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations


data <- DataDual(doseGrid=seq(25,300,25))
##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 must be 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)

##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 cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##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)))))}


##options for MCMC
options<-McmcOptions(burnin=10,step=1,samples=20)
##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,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)
##plot this simulated results
print(plot(mySim))