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DesignGrouped

Source: R/Design-class.R
DesignGrouped-class.Rd

[Experimental]

DesignGrouped combines two Design objects: one for the mono and one for the combo arm of a joint dose escalation design.

Usage

DesignGrouped(
  model,
  mono,
  combo = mono,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = !same_dose_for_start,
  same_dose_for_start = FALSE,
  stop_mono_with_combo = FALSE,
  ...
)

Arguments

model

(LogisticLogNormalGrouped)
see slot definition.

mono

(Design)
see slot definition.

combo

(Design)
see slot definition.

first_cohort_mono_only

(flag)
see slot definition.

same_dose_for_all

(flag)
see slot definition.

same_dose_for_start

(flag)
see slot definition.

stop_mono_with_combo

(flag)
whether the mono arm should be stopped when the combo arm is stopped (this makes sense when the only real trial objective is the recommended combo dose).

...

not used.

Details

  • Note that the model slots inside the mono and combo parameters are ignored (because we don't fit separate regression models for the mono and combo arms). Instead, the model parameter is used to fit a joint regression model for the mono and combo arms together.

  • same_dose_for_start = TRUE is useful as an option when we want to use same_dose_for_all = FALSE combined with first_cohort_mono_only = TRUE. This will allow to randomize patients to the mono and combo arms at the same dose as long as the selected dose for the cohorts stay the same. This can therefore further mitigate bias as long as possible between the mono and combo arms.

Slots

model

(LogisticLogNormalGrouped)
the model to be used, currently only one class is allowed.

mono

(Design)
defines the dose escalation rules for the mono arm, see details.

combo

(Design)
defines the dose escalation rules for the combo arm, see details.

first_cohort_mono_only

(flag)
whether first test one mono agent cohort, and then once its DLT data has been collected, we proceed from the second cohort onwards with concurrent mono and combo cohorts.

same_dose_for_all

(flag)
whether the lower dose of the separately determined mono and combo doses should be used as the next dose for both mono and combo in all cohorts.

same_dose_for_start

(flag)
indicates whether, when mono and combo are used in the same cohort for the first time, the same dose should be used for both. Note that this is different from same_dose_for_all which will always force them to be the same. If same_dose_for_all = TRUE, this is therefore ignored. See Details.

Note

Typically, end-users will not use the .DefaultDesignGrouped() function.

Examples

empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the joint model.
my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Rules to be used for both arms.
one_arm <- Design(
  model = .DefaultModelLogNormal(), # Ignored.
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)

# Initialize the design.
design <- DesignGrouped(
  model = my_model,
  mono = one_arm
)

# Alternative options: Here e.g.
# - use both mono in first cohort and afterwards have mono and combo in parallel,
# - in general allow different dose levels for the cohorts,
# - but for the start (i.e. second cohort) have the same dose for mono and combo.
# - Stop mono arm too, when combo arm is stopped.

design2 <- DesignGrouped(
  model = my_model,
  mono = one_arm,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = FALSE,
  same_dose_for_start = TRUE,
  stop_mono_with_combo = TRUE
)