Tidying CrmPackClass objects
Source: R/CrmPackClass-methods.R, R/Data-methods.R, R/Simulations-class.R, and 4 more
tidy.Rd![[Experimental]](figures/lifecycle-experimental.svg)
In the spirit of the broom package, provide a method to convert a
CrmPackClass object to a (list of) tibbles.
Following the principles of the broom package, convert a CrmPackClass
object to a (list of) tibbles. This is a basic, default representation.
A method that tidies a GeneralData object.
A method that tidies a Data object.
A method that tidies a DataGrouped object.
A method that tidies a DataDA object.
A method that tidies a DataDual object.
A method that tidies a DataParts object.
A method that tidies a DataMixture object.
A method that tidies a DataOrdinal object.
A method that tidies a LogisticIndepBeta object.
A method that tidies a Effloglog object.
Usage
tidy(x, ...)
# S4 method for class 'CrmPackClass'
tidy(x, ...)
# S4 method for class 'GeneralData'
tidy(x, ...)
# S4 method for class 'Data'
tidy(x, ...)
# S4 method for class 'DataGrouped'
tidy(x, ...)
# S4 method for class 'DataDA'
tidy(x, ...)
# S4 method for class 'DataDual'
tidy(x, ...)
# S4 method for class 'DataParts'
tidy(x, ...)
# S4 method for class 'DataMixture'
tidy(x, ...)
# S4 method for class 'DataOrdinal'
tidy(x, ...)
# S4 method for class 'Simulations'
tidy(x, ...)
# S4 method for class 'LogisticIndepBeta'
tidy(x, ...)
# S4 method for class 'Effloglog'
tidy(x, ...)
# S4 method for class 'IncrementsMaxToxProb'
tidy(x, ...)
# S4 method for class 'IncrementsRelative'
tidy(x, ...)
# S4 method for class 'CohortSizeDLT'
tidy(x, ...)
# S4 method for class 'CohortSizeMin'
tidy(x, ...)
# S4 method for class 'CohortSizeMax'
tidy(x, ...)
# S4 method for class 'CohortSizeRange'
tidy(x, ...)
# S4 method for class 'CohortSizeParts'
tidy(x, ...)
# S4 method for class 'IncrementsMin'
tidy(x, ...)
# S4 method for class 'IncrementsRelative'
tidy(x, ...)
# S4 method for class 'IncrementsRelativeDLT'
tidy(x, ...)
# S4 method for class 'IncrementsRelativeParts'
tidy(x, ...)
# S4 method for class 'NextBestNCRM'
tidy(x, ...)
# S4 method for class 'NextBestNCRMLoss'
tidy(x, ...)
# S4 method for class 'DualDesign'
tidy(x, ...)
# S4 method for class 'Samples'
tidy(x, ...)Value
A (list of) tibble(s) representing the object in tidy form.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The tibble::tibble object.
The list of tibble::tibble objects.
The list of tibble::tibble objects.
Examples
CohortSizeConst(3) %>% tidy()
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 3
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
# Create a sample Data object
sample_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
response = c(0, 0, 0, 0, 0, 1, NA, NA),
backfilled = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE)
)
#> Used default patient IDs!
# Tidy the Data object
tidied_data <- tidy(sample_data)
# Print the tidied data
print(tidied_data)
#> # A tibble: 8 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> 0
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> 0
#> 3 3 3 1.5 3 FALSE FALSE 8 41 <dbl [41]> 0
#> 4 4 4 3 4 FALSE FALSE 8 41 <dbl [41]> 0
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> 0
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> 1
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultData() %>% tidy()
#> # A tibble: 3 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 1 more variable: Backfilled <lgl>
.DefaultDataOrdinal() %>% tidy()
#> # A tibble: 10 × 11
#> ID Cohort Dose Placebo NObs NGrid DoseGrid XLevel Cat0 Cat1 Cat2
#> <int> <int> <dbl> <lgl> <int> <int> <list> <int> <lgl> <lgl> <lgl>
#> 1 1 1 10 FALSE 10 10 <dbl [10]> 1 TRUE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 TRUE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 TRUE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 TRUE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 6 6 5 50 FALSE 10 10 <dbl [10]> 5 FALSE TRUE FALSE
#> 7 7 5 50 FALSE 10 10 <dbl [10]> 5 TRUE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 TRUE FALSE FALSE
#> 9 9 6 60 FALSE 10 10 <dbl [10]> 6 FALSE TRUE FALSE
#> 10 10 6 60 FALSE 10 10 <dbl [10]> 6 FALSE FALSE TRUE
.DefaultDataGrouped() %>% tidy()
#> # A tibble: 3 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> NA
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> NA
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, Group <fct>
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 14
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl [41]> NA
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl [41]> NA
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl [41]> NA
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl [41]> NA
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl [41]> NA
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl [41]> NA
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl [41]> NA
#> # ℹ 4 more variables: Backfilled <lgl>, U <dbl>, T0 <dbl>, TMax <dbl>
.DefaultSimulations() %>% tidy()
#> $fit
#> $fit[[1]]
#> middle lower upper
#> 1 0.02543116 0.0001004479 0.1503507
#> 2 0.05574713 0.0015248492 0.2222523
#> 3 0.08388981 0.0054667211 0.2586646
#> 4 0.15237643 0.0288619739 0.3309126
#> 5 0.21896290 0.0698278380 0.4149312
#> 6 0.28228334 0.1124456490 0.4716041
#> 7 0.34090583 0.1634082164 0.5281649
#> 8 0.48287089 0.2730147984 0.6824787
#> 9 0.55155990 0.3072458718 0.7575930
#> 10 0.67925168 0.3555320575 0.8979877
#> 11 0.72838460 0.3828168636 0.9324968
#>
#>
#> $stop_report
#> # A tibble: 1 × 1
#> stop_report[,NA] [,NA] [,"≥ 3 cohorts dosed"] [,"P(0.2 ≤ prob(DLE | NBD) ≤ 0…¹
#> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE TRUE TRUE TRUE
#> # ℹ abbreviated name: ¹[,"P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5"]
#> # ℹ 1 more variable: stop_report[5] <lgl>
#>
#> $data
#> $data[[1]]
#> # A tibble: 20 × 11
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 3 2 FALSE FALSE 20 11 <dbl [11]> 1
#> 2 2 2 5 3 FALSE FALSE 20 11 <dbl [11]> 1
#> 3 3 3 10 4 FALSE FALSE 20 11 <dbl [11]> 1
#> 4 4 4 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 5 5 5 25 7 TRUE FALSE 20 11 <dbl [11]> 1
#> 6 6 6 25 7 FALSE FALSE 20 11 <dbl [11]> 1
#> 7 7 6 25 7 TRUE FALSE 20 11 <dbl [11]> 1
#> 8 8 6 25 7 FALSE FALSE 20 11 <dbl [11]> 1
#> 9 9 7 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 10 10 7 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 11 11 7 20 6 TRUE FALSE 20 11 <dbl [11]> 1
#> 12 12 8 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 13 13 8 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 14 14 8 20 6 FALSE FALSE 20 11 <dbl [11]> 1
#> 15 15 9 25 7 TRUE FALSE 20 11 <dbl [11]> 1
#> 16 16 9 25 7 TRUE FALSE 20 11 <dbl [11]> 1
#> 17 17 9 25 7 TRUE FALSE 20 11 <dbl [11]> 1
#> 18 18 10 15 5 FALSE FALSE 20 11 <dbl [11]> 1
#> 19 19 10 15 5 FALSE FALSE 20 11 <dbl [11]> 1
#> 20 20 10 15 5 FALSE FALSE 20 11 <dbl [11]> 1
#> # ℹ 1 more variable: Backfilled <lgl>
#>
#>
#> $doses
#> # A tibble: 1 × 1
#> doses
#> <dbl>
#> 1 20
#>
#> $seed
#> # A tibble: 1 × 1
#> seed
#> <int>
#> 1 819
#>
#> attr(,"class")
#> [1] "tbl_Simulations" "list"
.DefaultLogisticIndepBeta() %>% tidy()
#> $pseudoData
#> # A tibble: 2 × 3
#> Dose N Tox
#> <dbl> <int> <dbl>
#> 1 25 3 1.05
#> 2 300 3 1.8
#>
#> $data
#> # A tibble: 0 × 11
#> # ℹ 11 variables: ID <int>, Cohort <int>, Dose <dbl>, XLevel <int>, Tox <lgl>,
#> # Placebo <lgl>, NObs <int>, NGrid <int>, DoseGrid <list>, Response <int>,
#> # Backfilled <lgl>
#>
#> $params
#> # A tibble: 2 × 3
#> Param mean cov
#> <chr> <dbl> <named list>
#> 1 Phi1 -1.95 <dbl [2 × 2]>
#> 2 Phi2 0.412 <dbl [2 × 2]>
#>
#> attr(,"class")
#> [1] "tbl_LogisticIndepBeta" "list"
.DefaultEffloglog() %>% tidy()
#> $pseudoData
#> # A tibble: 2 × 2
#> Dose Response
#> <dbl> <dbl>
#> 1 25 1.22
#> 2 300 2.51
#>
#> $data
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Response
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <int>
#> 1 1 1 25 1 FALSE FALSE 8 12 <dbl [12]> NA
#> 2 2 2 50 2 FALSE FALSE 8 12 <dbl [12]> NA
#> 3 3 2 50 2 FALSE FALSE 8 12 <dbl [12]> NA
#> 4 4 3 75 3 FALSE FALSE 8 12 <dbl [12]> NA
#> 5 5 4 100 4 TRUE FALSE 8 12 <dbl [12]> NA
#> 6 6 4 100 4 TRUE FALSE 8 12 <dbl [12]> NA
#> 7 7 5 225 9 TRUE FALSE 8 12 <dbl [12]> NA
#> 8 8 6 300 12 TRUE FALSE 8 12 <dbl [12]> NA
#> # ℹ 2 more variables: Backfilled <lgl>, W <dbl>
#>
#> $params
#> # A tibble: 2 × 3
#> Param mean cov
#> <chr> <dbl> <named list>
#> 1 theta1 -2.82 <dbl [2 × 2]>
#> 2 theta2 2.71 <dbl [2 × 2]>
#>
#> attr(,"class")
#> [1] "tbl_Effloglog" "list"
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05)) %>% tidy()
#> # A tibble: 2 × 2
#> Grade Prob
#> <chr> <dbl>
#> 1 DLAE 0.2
#> 2 CRS 0.05
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 20 1
#> 2 20 Inf 3
.DefaultCohortSizeDLT() %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
.DefaultCohortSizeMin() %>% tidy()
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 10 1
#> 2 10 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMin" "tbl_CohortSizeMin" "list"
.DefaultCohortSizeMax() %>% tidy()
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 10 1
#> 2 10 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMax" "tbl_CohortSizeMax" "list"
.DefaultCohortSizeRange() %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 30 1
#> 2 30 Inf 3
CohortSizeParts(cohort_sizes = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 2
#> part cohort_size
#> <int> <int>
#> 1 1 1
#> 2 2 3
.DefaultIncrementsMin() %>% tidy()
#> [[1]]
#> # A tibble: 3 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 1 1
#> 2 1 3 0.33
#> 3 3 Inf 0.2
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 20 1
#> 2 20 Inf 0.33
#>
#> attr(,"class")
#> [1] "tbl_IncrementsMin" "tbl_IncrementsMin" "list"
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 20 1
#> 2 20 Inf 3
x <- .DefaultIncrementsRelativeDLT()
x %>% tidy()
#> # A tibble: 3 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 1 1
#> 2 1 3 0.33
#> 3 3 Inf 0.2
.DefaultIncrementsRelativeParts() %>% tidy()
#> $dlt_start
#> # A tibble: 1 × 1
#> dlt_start
#> <int>
#> 1 0
#>
#> $clean_start
#> # A tibble: 1 × 1
#> clean_start
#> <int>
#> 1 1
#>
#> $intervals
#> # A tibble: 2 × 1
#> intervals
#> <dbl>
#> 1 0
#> 2 2
#>
#> $increments
#> # A tibble: 2 × 1
#> increments
#> <dbl>
#> 1 2
#> 2 1
#>
#> attr(,"class")
#> [1] "tbl_IncrementsRelativeParts" "list"
NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
) %>%
tidy()
#> # A tibble: 3 × 4
#> Range min max max_prob
#> <chr> <dbl> <dbl> <dbl>
#> 1 Underdose 0 0.2 NA
#> 2 Target 0.2 0.35 NA
#> 3 Overdose 0.35 1 0.25
.DefaultNextBestNCRMLoss() %>% tidy()
#> # A tibble: 4 × 5
#> Range Lower Upper LossCoefficient MaxOverdoseProb
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Underdose 0 0.2 1 0.25
#> 2 Target 0.2 0.35 0 0.25
#> 3 Overdose 0.35 0.6 1 0.25
#> 4 Unacceptable 0.6 1 2 0.25
.DefaultDualDesign() %>% tidy()
#> $model
#> $sigma2betaW
#> # A tibble: 1 × 1
#> sigma2betaW
#> <dbl>
#> 1 0.01
#>
#> $rw1
#> # A tibble: 1 × 1
#> rw1
#> <lgl>
#> 1 TRUE
#>
#> $betaZ_params
#> # A tibble: 2 × 3
#> mean cov[,1] [,2] prec[,1] [,2]
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 0 1 0
#> 2 1 0 1 0 1
#>
#> $ref_dose
#> # A tibble: 1 × 1
#> ref_dose
#> <pstv_nmb>
#> 1 1
#>
#> $use_log_dose
#> # A tibble: 1 × 1
#> use_log_dose
#> <lgl>
#> 1 FALSE
#>
#> $sigma2W
#> # A tibble: 2 × 1
#> sigma2W
#> <dbl>
#> 1 0.1
#> 2 0.1
#>
#> $rho
#> # A tibble: 2 × 1
#> rho
#> <dbl>
#> 1 1
#> 2 1
#>
#> $use_fixed
#> # A tibble: 3 × 1
#> use_fixed
#> <lgl>
#> 1 FALSE
#> 2 FALSE
#> 3 TRUE
#>
#> $datanames
#> # A tibble: 5 × 1
#> datanames
#> <chr>
#> 1 nObs
#> 2 w
#> 3 x
#> 4 xLevel
#> 5 y
#>
#> $datanames_prior
#> # A tibble: 2 × 1
#> datanames_prior
#> <chr>
#> 1 nGrid
#> 2 doseGrid
#>
#> $sample
#> # A tibble: 5 × 1
#> sample
#> <chr>
#> 1 betaZ
#> 2 precW
#> 3 rho
#> 4 betaW
#> 5 delta
#>
#> attr(,"class")
#> [1] "tbl_DualEndpointRW" "list"
#>
#> $data
#> # A tibble: 0 × 12
#> # ℹ 12 variables: ID <int>, Cohort <int>, Dose <dbl>, XLevel <int>, Tox <lgl>,
#> # Placebo <lgl>, NObs <int>, NGrid <int>, DoseGrid <list>, Response <int>,
#> # Backfilled <lgl>, W <dbl>
#>
#> $stopping
#> $stop_list
#> $stop_list[[1]]
#> $target
#> # A tibble: 2 × 1
#> target
#> <dbl>
#> 1 0.9
#> 2 1
#>
#> $is_relative
#> # A tibble: 1 × 1
#> is_relative
#> <lgl>
#> 1 TRUE
#>
#> $prob
#> # A tibble: 1 × 1
#> prob
#> <dbl>
#> 1 0.5
#>
#> $report_label
#> # A tibble: 1 × 1
#> report_label
#> <chr>
#> 1 P(0.9 ≤ Biomarker ≤ 1) ≥ 0.5 (relative)
#>
#> attr(,"class")
#> [1] "tbl_StoppingTargetBiomarker" "list"
#>
#> $stop_list[[2]]
#> # A tibble: 1 × 2
#> nPatients report_label
#> <int> <chr>
#> 1 40 ≥ 40 patients dosed
#>
#>
#> $report_label
#> # A tibble: 1 × 1
#> report_label
#> <chr>
#> 1 NA
#>
#> attr(,"class")
#> [1] "tbl_StoppingAny" "list"
#>
#> $increments
#> # A tibble: 2 × 3
#> min max increment
#> <dbl> <dbl> <dbl>
#> 1 0 20 1
#> 2 20 Inf 0.33
#>
#> $pl_cohort_size
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 0
#>
#> $backfill
#> $cohort_size
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 3
#>
#> $opening
#> list()
#> attr(,"class")
#> [1] "tbl_OpeningNone" "list"
#>
#> $recruitment
#> list()
#> attr(,"class")
#> [1] "tbl_RecruitmentUnlimited" "list"
#>
#> $max_size
#> # A tibble: 1 × 1
#> max_size
#> <int>
#> 1 1000000
#>
#> $priority
#> # A tibble: 1 × 1
#> priority
#> <chr>
#> 1 highest
#>
#> attr(,"class")
#> [1] "tbl_Backfill" "list"
#>
#> $nextBest
#> $target
#> # A tibble: 2 × 1
#> target
#> <dbl>
#> 1 0.9
#> 2 1
#>
#> $overdose
#> # A tibble: 2 × 1
#> overdose
#> <dbl>
#> 1 0.35
#> 2 1
#>
#> $max_overdose_prob
#> # A tibble: 1 × 1
#> max_overdose_prob
#> <dbl>
#> 1 0.25
#>
#> $target_relative
#> # A tibble: 1 × 1
#> target_relative
#> <lgl>
#> 1 TRUE
#>
#> $target_thresh
#> # A tibble: 1 × 1
#> target_thresh
#> <dbl>
#> 1 0.01
#>
#> attr(,"class")
#> [1] "tbl_NextBestDualEndpoint" "list"
#>
#> $cohort_size
#> [[1]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 30 1
#> 2 30 Inf 3
#>
#> [[2]]
#> # A tibble: 2 × 3
#> min max cohort_size
#> <dbl> <dbl> <int>
#> 1 0 1 1
#> 2 1 Inf 3
#>
#> attr(,"class")
#> [1] "tbl_CohortSizeMax" "tbl_CohortSizeMax" "list"
#>
#> $startingDose
#> # A tibble: 1 × 1
#> startingDose
#> <dbl>
#> 1 3
#>
#> attr(,"class")
#> [1] "tbl_DualDesign" "list"
options <- McmcOptions(
burnin = 100,
step = 1,
samples = 2000
)
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
samples <- mcmc(emptydata, model, options)
samples %>% tidy()
#> $data
#> # A tibble: 2,000 × 10
#> Iteration Chain alpha0 alpha1 nChains nParameters nIterations nBurnin nThin
#> <int> <int> <dbl> <dbl> <int> <int> <int> <int> <int>
#> 1 1 1 -0.803 2.41 1 1 2100 100 1
#> 2 2 1 0.831 0.710 1 1 2100 100 1
#> 3 3 1 -1.03 1.06 1 1 2100 100 1
#> 4 4 1 -2.07 1.81 1 1 2100 100 1
#> 5 5 1 -1.21 2.04 1 1 2100 100 1
#> 6 6 1 0.350 1.96 1 1 2100 100 1
#> 7 7 1 0.598 0.594 1 1 2100 100 1
#> 8 8 1 -1.14 4.44 1 1 2100 100 1
#> 9 9 1 -0.169 0.916 1 1 2100 100 1
#> 10 10 1 -0.524 6.91 1 1 2100 100 1
#> # ℹ 1,990 more rows
#> # ℹ 1 more variable: parallel <lgl>
#>
#> $options
#> # A tibble: 1 × 5
#> iterations burnin step rng_kind rng_seed
#> <int> <int> <int> <chr> <int>
#> 1 2100 100 1 NA NA
#>
#> attr(,"class")
#> [1] "tbl_Samples" "list"