summary,PseudoSimulations-method | R Documentation |

Summarize the simulations, relative to a given truth

## S4 method for signature 'PseudoSimulations' summary(object, truth, targetEndOfTrial = 0.3, targetDuringTrial = 0.35, ...)

`object` |
the |

`truth` |
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity |

`targetEndOfTrial` |
the target probability of DLE wanted to achieve at the end of a trial |

`targetDuringTrial` |
the target probability of DLE wanted to achieve during a trial |

`...` |
Additional arguments can be supplied here for |

an object of class `PseudoSimulationsSummary`

##If only DLE responses are considered in the simulations ##Specified your simulations when no DLE samples are used ## data set with dose levels from 25 to 300 with increments 25 data <- Data(doseGrid=seq(25,300,25)) ##The design only incorporate DLE responses and DLE samples are involved ##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data) ##Then the escalation rule tdNextBest <- NextBestTD(targetDuringTrial=0.35, targetEndOfTrial=0.3) ##Then the starting data, an empty data set emptydata<-Data(doseGrid=seq(25,300,25)) ## 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(for details please refer to the 'TDDesign' example) design <- TDDesign(model=model, nextBest=tdNextBest, stopping=myStopping, increments=myIncrements, cohortSize=mySize, data=data,startingDose=25) ##Specify the truth of the DLE responses myTruth <- function(dose) { model@prob(dose, phi1=-53.66584, phi2=10.50499) } ##(Please refer to desgin-method 'simulate TDDesign' examples for details) ##For illustration purpose only 1 simulation is produced (nsim=1). mySim <- simulate(design, args=NULL, truth=myTruth, nsim=1, seed=819, parallel=FALSE) ##Then produce a summary of your simulations summary(mySim, truth=myTruth) ##If DLE samples are involved ##specify the next best tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35, targetEndOfTrial=0.3, derive=function(TDsamples){quantile(TDsamples,probs=0.3)}) ##The design design <- TDsamplesDesign(model=model, nextBest=tdNextBest, stopping=myStopping, increments=myIncrements, cohortSize=mySize, data=data,startingDose=25) ##options for MCMC ##For illustration purpose, we will use 50 burn-ins to generate 200 samples options<-McmcOptions(burnin=50,step=2,samples=200) ##The simulations ## For illustration purpose we will only generate 2 trials (nsim=2) mySim <- simulate(design, args=NULL, truth=myTruth, nsim=2, seed=819, mcmcOptions=options, parallel=FALSE) ##Then produce a summary of your simulations summary(mySim, truth=myTruth)

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