DesignOrdinal

DesignOrdinal is the class for rule-based ordinal designs. The difference between this class and its parent RuleDesignOrdinal class is that the DesignOrdinal class contains additional model, stopping, increments and pl_cohort_size slots.

Usage

DesignOrdinal(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(0L)),
  ...
)

.DefaultDesignOrdinal()

Arguments

model

(LogisticLogNormalOrdinal)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

...

Arguments passed on to RuleDesignOrdinal

next_best

(NextBestOrdinal)
see slot definition.

cohort_size

(CohortSize)
see slot definition.

data

(DataOrdinal)
see slot definition.

starting_dose

(number)
see slot definition.

Details

Please note that stopping, increments or cohort size rules need to be wrapped into the corresponding StoppingOrdinal, IncrementsOrdinal or CohortSizeOrdinal classes, before a successful evaluation of the corresponding methods can take place. Note also that these wrappers cannot be nested, i.e., you cannot have an IncrementsOrdinal inside another IncrementsOrdinal (which also would not make sense) because it would not be clear which event grade to use for the methods calculation. However, multiple rules can be combined using the operators defined for these classes, e.g., StoppingOrdinal(1L, rule1 & rule2) | StoppingOrdinal(2L, rule3).

Slots

model

(LogisticLogNormalOrdinal)
the 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).

Note

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

Examples

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 <- CohortSizeOrdinal(1L, maxSize(my_size1, my_size2))

my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- StoppingOrdinal(1L, (my_stopping1 & my_stopping2) | my_stopping3)

# Initialize the design.
design <- DesignOrdinal(
  model = LogisticLogNormalOrdinal(
    mean = c(-3, -4, 1),
    cov = diag(c(3, 4, 1)),
    ref_dose = 50
  ),
  next_best = NextBestOrdinal(
    1L,
    NextBestNCRM(
      target = c(0.2, 0.35),
      overdose = c(0.35, 1),
      max_overdose_prob = 0.25
    )
  ),
  stopping = my_stopping,
  increments = IncrementsOrdinal(
    1L,
    IncrementsRelative(
      intervals = c(0, 20),
      increments = c(1, 0.33)
    )
  ),
  cohort_size = my_size,
  data = DataOrdinal(
    doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100),
    yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L)
  ),
  starting_dose = 3
)