Learn R Programming
crmPack (version 2.0.0)

size: Size of an Object

Description

[Stable]

A method that computes the size of a given object. This can be for instance a size of a MCMC sample, or the size of a cohort. See the help of a specific method for more details.

Usage

size(object, ...)
# S4 method for McmcOptions
size(object, ...)
# S4 method for CohortSizeRange
size(object, dose, data)
# S4 method for CohortSizeDLT
size(object, dose, data)
# S4 method for CohortSizeMax
size(object, dose, data)
# S4 method for CohortSizeMin
size(object, dose, data)
# S4 method for CohortSizeConst
size(object, dose, ...)
# S4 method for CohortSizeParts
size(object, dose, data)
# S4 method for CohortSizeOrdinal
size(object, dose, data, ...)
# S4 method for Samples
size(object, ...)

Value

A size of a given object.

Arguments

object

(McmcOptions or Samples or CohortSize)
an object for which the size is computed.

...

not used.

dose

(numeric) the next dose.

data

the data input, an object of class DataOrdinal.

Functions

  • size(McmcOptions): compute the number of MCMC samples based on McmcOptions object.

  • size(CohortSizeRange): Determines the size of the next cohort based on the range into which the next dose falls into.

  • size(CohortSizeDLT): Determines the size of the next cohort based on the number of DLTs so far.

  • size(CohortSizeMax): Determines the size of the next cohort based on maximum of multiple cohort size rules.

  • size(CohortSizeMin): Determines the size of the next cohort based on minimum of multiple cohort size rules.

  • size(CohortSizeConst): Constant cohort size.

  • size(CohortSizeParts): Determines the size of the next cohort based on the parts.

  • size(CohortSizeOrdinal): Determines the size of the next cohort in a ordinal CRM trial.

  • size(Samples): get the number of MCMC samples from Samples object.

Examples

Run this code
# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)

size(my_options)
# nolint start

# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 for doses <10
#   - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 if no DLTs were yet observed
#   - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 10),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
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(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value)

# nolint end
# nolint start

# create an object of class 'DataParts'
data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
mySize <- CohortSizeParts(cohort_sizes = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
CohortSizeOrdinal(
  grade = 1L,
  rule = CohortSizeRange(intervals = c(0L, 30L), cohort_size = c(1L, 3L))
)
# Set up the MCMC option in order to have a burn-in of 100 iterations and
# then take every other iteration up to a collection of 200 samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 200)

my_samples <- Samples(
  data = list(alpha = rnorm(200), beta = rnorm(200)),
  options = my_options
)

size(my_samples)

Run the code above in your browser using DataLab