This class has special requirements for the `model`

and `data`

slots in comparison to the parent class `Design`

:

`model`

the model to be used, an object of class

`DualEndpoint`

`data`

what is the dose grid, any previous data, etc., contained in an object of class

`DataDual`

Note that the

`NextBest`

slot can be of any class, this allows for easy comparison with recommendation methods that don't use the biomarker information.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
# 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 = mySize,
startingDose = 3)
``` |

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