size: Determine the size of the next cohort

Description Usage Arguments Value Methods (by class) Examples

Description

This function determines the size of the next cohort.

Usage

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size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeRange,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeDLT,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMax,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMin,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeConst,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeParts,ANY,DataParts'
size(cohortSize, dose, data, ...)

Arguments

cohortSize

The rule, an object of class CohortSize

dose

the next dose

data

The data input, an object of class Data

...

additional arguments

Value

the size as integer value

Methods (by class)

Examples

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# 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),
                           refDose=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),
                           maxOverdoseProb=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),
                          cohortSize=c(1, 3))

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


# 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),
                           refDose=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),
                           maxOverdoseProb=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(DLTintervals = c(0, 1),
                        cohortSize = c(1, 3))

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


# 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),
                           refDose=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),
                           maxOverdoseProb=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),
                           cohortSize = 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(DLTintervals=c(0, 1),
                         cohortSize=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)




# 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),
                           refDose=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),
                           maxOverdoseProb=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),
                           cohortSize = 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(DLTintervals=c(0, 1),
                         cohortSize=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)




# 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),
                           refDose=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),
                           maxOverdoseProb=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, data = data)




# 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),
                           refDose=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(dltStart=0,
                                        cleanStart=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),
                           maxOverdoseProb=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(sizes=c(1,3))

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

crmPack documentation built on June 13, 2019, 9:02 a.m.