plot-PseudoDualSimulations-missing-method: Plot simulations

In crmPack: Object-Oriented Implementation of CRM Designs

Plot simulations

Description

Summarize the simulations with plots

Usage

## S4 method for signature 'PseudoDualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2"), ...)

Arguments

x	the PseudoDualSimulations object we want to plot from
y	missing
type	the type of plots you want to obtain.
...	not used

Details

This plot method can be applied to PseudoDualSimulations 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

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

Value

A single ggplot object if a single plot is asked for, otherwise a gridExtra{gTree} object.

Examples

##obtain the plot 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 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##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,
                 seed=819,
                 parallel=FALSE)

##plot the simulation results
print(plot(mySim))

##If DLE and efficacy samples are involved
##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 design of 'DualResponsesSamplesDesign' class
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##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(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=1,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)
##plot the simulation results
print(plot(mySim))

crmPack documentation built on Sept. 3, 2022, 1:05 a.m.