plot-PseudoDualSimulationsSummary-missing-method: Plot the summary of Pseudo Dual Simulations summary

In crmPack: Object-Oriented Implementation of CRM Designs

Plot the summary of Pseudo Dual Simulations summary

Description

This plot method can be applied to PseudoDualSimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,PseudoSimulationsSummary,missing-method plus:

meanEffFit

Plot showing the fitted dose-efficacy curve. If no samples are involved, only the average fitted dose-efficacy curve across the trials will be ploted. If samples (DLE and efficacy) are involved, the average fitted dose-efficacy curve across the trials, together with the 95% credibility interval; and comparison with the assumed truth (as specified by the trueEff argument to summary,PseudoDualSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'PseudoDualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit",
    "meanEffFit"),
  ...
)

Arguments

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

Value

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

Examples

##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))

crmPack documentation built on June 26, 2024, 5:07 p.m.