Determine the safety window length of the next cohort

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

Usage

windowLength(safetyWindow, size, ...)

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

# S4 method for 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 (gap, follow, follow_min)

Functions

windowLength(SafetyWindowSize): Determine safety window length based on the cohort size
windowLength(SafetyWindowConst): 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)
#> Used default patient IDs!
#> Used best guess cohort indices!

# 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),
                           max_overdose_prob=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(gap = list(c(7,3),c(9,5)),
                                   size = c(1,4),
                                   follow = 7,
                                   follow_min = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size=sizeRecommendation)
#> $patientGap
#> [1] 0 7 3
#> 
#> $patientFollow
#> [1] 7
#> 
#> $patientFollowMin
#> [1] 14
#> 

# 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)
#> Used default patient IDs!
#> Used best guess cohort indices!

# 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),
                           max_overdose_prob=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(gap = c(7,3),
                                    follow = 7,
                                    follow_min = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size=sizeRecommendation)
#> $patientGap
#> [1] 0 7 3
#> 
#> $patientFollow
#> [1] 7
#> 
#> $patientFollowMin
#> [1] 14
#> 

# nolint end