TDDesign-class | R Documentation |
TDDesign
TDDesign
is the class of design based only on DLT responses using
ModelTox
class model (i.e. LogisticIndepBeta
) without MCMC samples.
TDDesign(
model,
stopping,
increments,
pl_cohort_size = CohortSizeConst(0L),
...
)
.DefaultTDDesign()
model |
( |
stopping |
( |
increments |
( |
pl_cohort_size |
( |
... |
Arguments passed on to
|
model
(ModelTox
)
the pseudo DLT model to be used.
stopping
(Stopping
)
stopping rule(s) for the trial.
increments
(Increments
)
how to control increments between dose levels.
pl_cohort_size
(CohortSize
)
rules for the cohort sizes for placebo,
if any planned (defaults to constant 0 placebo patients).
Typically, end users will not use the .DefaultTDDesign()
function.
empty_data <- Data(doseGrid = seq(25, 300, 25))
my_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = empty_data
)
# The escalation rule.
my_next_best <- NextBestTD(
prob_target_drt = 0.35,
prob_target_eot = 0.3
)
my_size <- CohortSizeConst(size = 3)
# The increments for the dose-escalation process:
# the maximum increase of 200% for doses up to the maximum dose in grid,
# the maximum increase of 200% for dose above the maximum dose in grid.
my_increments <- IncrementsRelative(
intervals = range(empty_data@doseGrid),
increments = c(2, 2)
)
# Stop when the maximum sample size of 36 patients is reached.
my_stopping <- StoppingMinPatients(nPatients = 36)
# The design with all the above information and starting with a dose of 25.
# This design incorporates only DLT responses and no DLT samples are involved
# during the simulation.
design <- TDDesign(
model = my_model,
stopping = my_stopping,
increments = my_increments,
nextBest = my_next_best,
cohort_size = my_size,
data = empty_data,
startingDose = 25
)
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