windowLength: Determine the safety window length of the next cohort

In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description

This function determines the safety window length of the next cohort.

Usage

windowLength(safetyWindow, size, ...)

## S4 method for signature 'SafetyWindowSize'
windowLength(safetyWindow, size, data, ...)

## S4 method for signature 'SafetyWindowConst'
windowLength(safetyWindow, size, ...)

Arguments

safetyWindow	The rule, an object of class SafetyWindow
size	The next cohort size
...	additional arguments
data	The data input, an object of class DataDA

Value

the windowLength as a list of safety window parameters (patientGap, patientFollow, patientFollowMin)

Functions

• windowLength,SafetyWindowSize-method: Determine safety window length based on the cohort size

• windowLength,SafetyWindowConst-method: Constant safety window length

Examples

# nolint start

# Create the data
data <- DataDA(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
               y=c(0, 0, 1, 1, 0, 0, 1, 0),
               doseGrid=
                 c(0.1, 0.5, 1.5, 3, 6,
                   seq(from=10, to=80, by=2)),
               u=c(42,30,15,5,20,25,30,60),
               t0=c(0,15,30,40,55,70,75,85),
               Tmax=60)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior<-function(k){
  npiece_/(data@Tmax*(npiece_-k+0.5))
}

model<-DALogisticLogNormal(mean=c(-0.85,1),
                           cov=matrix(c(1,-0.5,-0.5,1),nrow=2),
                           ref_dose=56,
                           npiece=npiece_,
                           l=as.numeric(t(apply(as.matrix(c(1:npiece_),1,npiece_),2,lambda_prior))),
                           C_par=2)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=200)
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)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size=3)

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

# Rule for the safety window length:
#   -having patientGap as (0,7,3,3,...) for cohort size <4
#   -and having patientGap as (0,9,5,5,...) for cohort size >=4
myWindowLength <- SafetyWindowSize(patientGap = list(c(7,3),c(9,5)),
                                   sizeInterval = c(1,4),
                                   patientFollow = 7,
                                   patientFollowMin = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size=sizeRecommendation)

# nolint end
# nolint start

# Create the data
data <- DataDA(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
               y=c(0, 0, 1, 1, 0, 0, 1, 0),
               doseGrid=
                 c(0.1, 0.5, 1.5, 3, 6,
                   seq(from=10, to=80, by=2)),
               u=c(42,30,15,5,20,25,30,60),
               t0=c(0,15,30,40,55,70,75,85),
               Tmax=60)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior<-function(k){
  npiece_/(data@Tmax*(npiece_-k+0.5))
}

model<-DALogisticLogNormal(mean=c(-0.85,1),
                           cov=matrix(c(1,-0.5,-0.5,1),nrow=2),
                           ref_dose=56,
                           npiece=npiece_,
                           l=as.numeric(t(apply(as.matrix(c(1:npiece_),1,npiece_),2,lambda_prior))),
                           C_par=2)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=200)
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)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size=3)

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

# Rule for having safety window length with constant safety window parameters
myWindowLength <- SafetyWindowConst(patientGap = c(7,3),
                                    patientFollow = 7,
                                    patientFollowMin = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size=sizeRecommendation)

# nolint end

0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.