Description Usage Arguments Details Value Examples

Graphical display of the simulation summary

1 2 3 |

`x` |
the |

`y` |
missing |

`type` |
the types of plots you want to obtain. |

`...` |
not used |

This plot method can be applied to `SimulationsSummary`

objects in order to summarize them graphically. Possible `type`

of
plots at the moment are those listed in
`plot,GeneralSimulationsSummary,missing-method`

plus:

- meanFit
Plot showing the average fitted dose-toxicity curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the

`truth`

argument to`summary,Simulations-method`

)

You can specify any subset of these in the `type`

argument.

A single `ggplot`

object if a single plot is
asked for, otherwise a `gridExtra{gTree}`

object.

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# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
# Initialize the CRM model
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
refDose=56)
# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(intervals=c(0, 30),
cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)
# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
# Initialize the design
design <- Design(model=model,
nextBest=myNextBest,
stopping=myStopping,
increments=myIncrements,
cohortSize=mySize,
data=emptydata,
startingDose=3)
## define the true function
myTruth <- function(dose)
{
model@prob(dose, alpha0=7, alpha1=8)
}
# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(burnin=10,
step=1,
samples=100)
time <- system.time(mySims <- simulate(design,
args=NULL,
truth=myTruth,
nsim=1,
seed=819,
mcmcOptions=options,
parallel=FALSE))[3]
# Plot the Summary of the Simulations
plot(summary(mySims,truth=myTruth))
``` |

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