Tidying CrmPackClass objects
Source: R/CrmPackClass-methods.R, R/Data-methods.R, R/Simulations-class.R, and 3 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 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.
Usage
tidy(x, ...)
# S4 method for CrmPackClass
tidy(x, ...)
# S4 method for GeneralData
tidy(x, ...)
# S4 method for DataGrouped
tidy(x, ...)
# S4 method for DataDA
tidy(x, ...)
# S4 method for DataDual
tidy(x, ...)
# S4 method for DataParts
tidy(x, ...)
# S4 method for DataMixture
tidy(x, ...)
# S4 method for DataOrdinal
tidy(x, ...)
# S4 method for Simulations
tidy(x, ...)
# S4 method for IncrementsRelative
tidy(x, ...)
# S4 method for CohortSizeDLT
tidy(x, ...)
# S4 method for CohortSizeMin
tidy(x, ...)
# S4 method for CohortSizeMax
tidy(x, ...)
# S4 method for CohortSizeRange
tidy(x, ...)
# S4 method for CohortSizeParts
tidy(x, ...)
# S4 method for IncrementsMin
tidy(x, ...)
# S4 method for IncrementsRelative
tidy(x, ...)
# S4 method for IncrementsRelativeDLT
tidy(x, ...)
# S4 method for IncrementsRelativeParts
tidy(x, ...)
# S4 method for NextBestNCRM
tidy(x, ...)
# S4 method for NextBestNCRMLoss
tidy(x, ...)
# S4 method for DualDesign
tidy(x, ...)
# S4 method for Samples
tidy(x, ...)Value
A (list of) tibble(s) representing the object in tidy form.
The tibble object.
The tibble object.
The tibble object.
The tibble object.
The tibble object.
The tibble object.
The tibble object.
Examples
CohortSizeConst(3) %>% tidy()
#> # A tibble: 1 × 1
#> size
#> <int>
#> 1 3
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultData() %>% tidy()
.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 FALSE FALSE FALSE
#> 2 2 2 20 FALSE 10 10 <dbl [10]> 2 FALSE FALSE FALSE
#> 3 3 3 30 FALSE 10 10 <dbl [10]> 3 FALSE FALSE FALSE
#> 4 4 4 40 FALSE 10 10 <dbl [10]> 4 FALSE FALSE FALSE
#> 5 5 5 50 FALSE 10 10 <dbl [10]> 5 FALSE 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 FALSE FALSE FALSE
#> 8 8 6 60 FALSE 10 10 <dbl [10]> 6 FALSE 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 × 10
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid Group
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <fct>
#> 1 1 1 1 1 FALSE FALSE 3 11 <dbl [11]> mono
#> 2 2 2 3 2 FALSE FALSE 3 11 <dbl [11]> mono
#> 3 3 3 5 3 FALSE FALSE 3 11 <dbl [11]> combo
.DefaultDataDA() %>% tidy()
#> # A tibble: 8 × 12
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid U T0 TMax
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list> <dbl> <dbl> <dbl>
#> 1 1 1 0.1 1 FALSE FALSE 8 41 <dbl> 42 0 60
#> 2 2 2 0.5 2 FALSE FALSE 8 41 <dbl> 30 15 60
#> 3 3 3 1.5 3 TRUE FALSE 8 41 <dbl> 15 30 60
#> 4 4 4 3 4 TRUE FALSE 8 41 <dbl> 5 40 60
#> 5 5 5 6 5 FALSE FALSE 8 41 <dbl> 20 55 60
#> 6 6 6 10 6 FALSE FALSE 8 41 <dbl> 25 70 60
#> 7 7 6 10 6 TRUE FALSE 8 41 <dbl> 30 75 60
#> 8 8 6 10 6 FALSE FALSE 8 41 <dbl> 60 85 60
.DefaultSimulations() %>% tidy()
#> $fit
#> $fit[[1]]
#> middle lower upper
#> 1 0.01887575 0.0001193348 0.09675264
#> 2 0.04507575 0.0015056681 0.15912747
#> 3 0.07049454 0.0046295997 0.20853090
#> 4 0.13447297 0.0238613716 0.29866770
#> 5 0.19814212 0.0534771537 0.38708074
#> 6 0.25933311 0.0942723956 0.46018312
#> 7 0.31634564 0.1280002722 0.53712561
#> 8 0.45575018 0.2177224191 0.70977550
#> 9 0.52397101 0.2550584651 0.79536591
#> 10 0.65353869 0.3344488409 0.91310570
#> 11 0.70496930 0.3646526312 0.94361617
#>
#>
#> $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: 22 × 9
#> ID Cohort Dose XLevel Tox Placebo NObs NGrid DoseGrid
#> <int> <int> <dbl> <int> <lgl> <lgl> <int> <int> <list>
#> 1 1 1 3 2 FALSE FALSE 22 11 <dbl [11]>
#> 2 2 2 5 3 FALSE FALSE 22 11 <dbl [11]>
#> 3 3 3 10 4 FALSE FALSE 22 11 <dbl [11]>
#> 4 4 4 20 6 TRUE FALSE 22 11 <dbl [11]>
#> 5 5 5 15 5 FALSE FALSE 22 11 <dbl [11]>
#> 6 6 5 15 5 FALSE FALSE 22 11 <dbl [11]>
#> 7 7 5 15 5 FALSE FALSE 22 11 <dbl [11]>
#> 8 8 6 25 7 FALSE FALSE 22 11 <dbl [11]>
#> 9 9 6 25 7 TRUE FALSE 22 11 <dbl [11]>
#> 10 10 6 25 7 FALSE FALSE 22 11 <dbl [11]>
#> # ℹ 12 more rows
#>
#>
#> $doses
#> # A tibble: 1 × 1
#> doses
#> <dbl>
#> 1 20
#>
#> $seed
#> # A tibble: 1 × 1
#> seed
#> <int>
#> 1 819
#>
#> attr(,"class")
#> [1] "tbl_Simulations" "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
.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 × 10
#> # ℹ 10 variables: ID <int>, Cohort <int>, Dose <dbl>, XLevel <int>, Tox <lgl>,
#> # Placebo <lgl>, NObs <int>, NGrid <int>, DoseGrid <list>, 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
#>
#> $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 0.0323 2.40 1 1 2100 100 1
#> 2 2 1 -0.957 1.25 1 1 2100 100 1
#> 3 3 1 -1.21 23.8 1 1 2100 100 1
#> 4 4 1 -0.994 1.16 1 1 2100 100 1
#> 5 5 1 0.362 0.636 1 1 2100 100 1
#> 6 6 1 -0.258 3.43 1 1 2100 100 1
#> 7 7 1 -1.24 6.34 1 1 2100 100 1
#> 8 8 1 -1.26 7.58 1 1 2100 100 1
#> 9 9 1 -1.75 8.91 1 1 2100 100 1
#> 10 10 1 -1.06 0.904 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"