plot,DualSimulations,missing-method | R Documentation |

This plot method can be applied to `DualSimulations`

objects in order to summarize them graphically. In addition to the standard
plot types, there is

- sigma2W
Plot a boxplot of the final biomarker variance estimates in the simulated trials

- rho
Plot a boxplot of the final correlation estimates in the simulated trials

```
## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)
```

`x` |
the |

`y` |
missing |

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

`...` |
not used |

A single `ggplot`

object if a single plot is
asked for, otherwise a `gtable`

object.

```
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
# Initialize the CRM model
model <- DualEndpointRW(mu = c(0, 1),
Sigma = matrix(c(1, 0, 0, 1), nrow=2),
sigma2betaW = 0.01,
sigma2W = c(a=0.1, b=0.1),
rho = c(a=1, b=1),
smooth="RW1")
# Choose the rule for selecting the next dose
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
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
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
# Initialize the design
design <- DualDesign(model = model,
data = emptydata,
nextBest = myNextBest,
stopping = myStopping,
increments = myIncrements,
cohortSize = CohortSizeConst(3),
startingDose = 3)
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
dose <- dose/scal
e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}
trueBiomarker <- function(dose)
{
betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}
trueTox <- function(dose)
{
pnorm((dose-60)/10)
}
# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)
# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
##Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
trueTox=trueTox,
trueBiomarker=trueBiomarker,
sigma2W=0.01,
rho=0,
nsim=1,
parallel=FALSE,
seed=3,
startingDose=6,
mcmcOptions =
McmcOptions(burnin=5,
step=1,
samples=20))
# Plot the results of the simulation
print(plot(mySims))
```

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