simulate,TDDesign-method | R Documentation |

`TDDesign`

where model used are of
`ModelTox`

class object and no samples are involved.This is a methods to simulate dose escalation procedure only using the DLE responses.
This is a method based on the `TDDesign`

where model used are of
`ModelTox`

class object and no samples are involved.

## S4 method for signature 'TDDesign' simulate( object, nsim = 1L, seed = NULL, truth, args = NULL, firstSeparate = FALSE, parallel = FALSE, nCores = min(parallel::detectCores(), 5), ... )

`object` |
the |

`nsim` |
the number of simulations (default :1) |

`seed` |
see |

`truth` |
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` |
data frame with arguments for the |

`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. |

`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 |

an object of class `PseudoSimulations`

@export @keywords methods

##Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used ##The design comprises a model, the escalation rule, starting data, ##a cohort size and a starting dose ##Define your data set first using an empty 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) doseRecommendation<-nextBest(tdNextBest, doselimit=max(data@doseGrid), model=model, data=data) ##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) } ##then plot the truth to see how the truth dose-DLE curve look like curve(myTruth(x), from=0, to=300,ylim=c(0,1)) ##For illustration purpose only 1 simulation is produced (nsim=1). mySim <- simulate(object=design, args=NULL, truth=myTruth, nsim=1, seed=819, parallel=FALSE)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.