RuleDesignOrdinal
is the class for rule-based designs. The difference between
this class and the DesignOrdinal
class is that RuleDesignOrdinal
does not contain model
, stopping
and increments
slots.
Slots
next_best
(
NextBestOrdinal
)
how to find the next best dose.cohort_size
(
CohortSizeOrdinal
)
rules for the cohort sizes.data
(
DataOrdinal
)
specifies dose grid, any previous data, etc.starting_dose
(
number
)
the starting dose, it must lie on the dose grid indata
.
Examples
RuleDesignOrdinal(
next_best = NextBestOrdinal(
1L,
NextBestMTD(
target = 0.25,
derive = function(x) median(x, na.rm = TRUE)
)
),
cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(size = 3L)),
data = DataOrdinal(doseGrid = c(5, 10, 15, 25, 35, 50, 80)),
starting_dose = 5
)
#> An object of class "RuleDesignOrdinal"
#> Slot "next_best":
#> An object of class "NextBestOrdinal"
#> Slot "grade":
#> [1] 1
#>
#> Slot "rule":
#> An object of class "NextBestMTD"
#> Slot "target":
#> [1] 0.25
#>
#> Slot "derive":
#> function (x)
#> median(x, na.rm = TRUE)
#> <environment: 0x55c95ab5f550>
#>
#>
#>
#> Slot "cohort_size":
#> An object of class "CohortSizeOrdinal"
#> Slot "grade":
#> [1] 1
#>
#> Slot "rule":
#> An object of class "CohortSizeConst"
#> Slot "size":
#> [1] 3
#>
#>
#>
#> Slot "data":
#> An object of class "DataOrdinal"
#> Slot "x":
#> numeric(0)
#>
#> Slot "y":
#> integer(0)
#>
#> Slot "doseGrid":
#> [1] 5 10 15 25 35 50 80
#>
#> Slot "nGrid":
#> [1] 7
#>
#> Slot "xLevel":
#> integer(0)
#>
#> Slot "yCategories":
#> No DLT DLT
#> 0 1
#>
#> Slot "placebo":
#> [1] FALSE
#>
#> Slot "ID":
#> integer(0)
#>
#> Slot "cohort":
#> integer(0)
#>
#> Slot "nObs":
#> [1] 0
#>
#>
#> Slot "starting_dose":
#> [1] 5
#>