Description
![[Stable]](figures/lifecycle-stable.svg?package=crmPack&version=2.0.0)
![[Experimental]](figures/lifecycle-experimental.svg?package=crmPack&version=2.0.0)
Usage
Value
Arguments
Functions
Examples
Run this code## Example of combining stopping rules with '&' and/or '|' operators
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3
# Example of usage for `StoppingMissingDose` StopTrial class.
# Create the data.
my_data <- Data(
x = c(0.01, 0.1, 0.5, 3, 6, 10, 10, 10),
y = c(0, 1, 1, 0, 0, 0, 0, 1),
cohort = c(1, 1, 2, 3, 4, 5, 5, 5),
ID = 1:8,
doseGrid = c(
0.01,
0.1,
0.5,
1.5,
3,
6,
seq(from = 10, to = 80, by = 2)
),
placebo = TRUE
)
# Initialize the CRM model used to model the data.
my_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.
my_options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose based
# on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
target = c(0.1, 0.25),
overdose = c(0.2, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
my_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Define the stopping rule such that the study would be stopped if there is
# no safe active dose returned from dose_recommendation.
my_stopping <- StoppingMissingDose()
my_stopping <- StoppingAny(
stop_list = c(
StoppingMinPatients(nPatients = 16),
StoppingMissingDose()
)
)
# Evaluate if to stop the trial.
stopTrial(
stopping = my_stopping,
dose = dose_recommendation$value,
data = my_data,
model = my_model
)
# nolint start
# Create some data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)
# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(
stop_list = c(myStopping1, myStopping2, myStopping3),
summary = any
)
# Evaluate if to stop the Trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
# nolint start
# Create some 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
)
# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)
# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3
# Evaluate if to stop the Trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
# nolint start
# Create some data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)
# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAny'
myStopping <- (myStopping1 | myStopping2) | myStopping3
# Evaluate if to stop the Trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
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
)
# Define the stopping rule such that the study would be stopped if at least 3
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3, percentage = 0.2)
# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Define the stopping rule such that the study would be stopped if at least 9
# patients were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingPatientsNearDose(nPatients = 9, percentage = 0.2)
# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$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
)
# Define the stopping rule such that the study would be stopped if at least 6
# cohorts were already dosed
myStopping <- StoppingMinCohorts(nCohorts = 6)
# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)
# nolint end
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Define the stopping rule such that the study would be stopped if at least 20
# patients were already dosed
myStopping <- StoppingMinPatients(nPatients = 20)
# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$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
)
# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
myStopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
# Evaluate if to stop the trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
# Define the stopping rule such that the study would be stopped if there is at least
# 0.9 probability that MTD > 0.5*next_best_dose. Here MTD is defined as the dose for
# which prob(DLE)=0.33
myStopping <- StoppingMTDdistribution(target = 0.33, thresh = 0.5, prob = 0.9)
# Evaluate if to stop the trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
# Create the data.
my_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.
my_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.
my_options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000,
rng_kind = "Mersenne-Twister",
rng_seed = 94
)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
my_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Define the stopping rule such that the study would be stopped if the
# the MTD can be estimated with sufficient precision, i.e. if robust coefficient
# of variation is below 40%.
my_stopping <- StoppingMTDCV(target = 0.3, thresh_cv = 40)
# Evaluate if to stop the trial.
stopTrial(
stopping = my_stopping,
dose = dose_recommendation$value,
samples = my_samples,
model = my_model,
data = my_data
)
# Create the data.
data <- Data(
x = c(0.1, 0.1, 0.1),
y = c(0, 0, 1),
cohort = c(1, 1, 1),
doseGrid = c(
0.1,
0.5,
1.5,
3,
6,
seq(from = 10, to = 80, by = 2)
),
ID = 1:3
)
# Initialize the CRM model used to model the data.
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
options <- McmcOptions(
burnin = 100,
step = 2,
samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
my_next_best,
doselimit = next_max_dose,
samples = samples,
model = model,
data = data
)
# Define the stopping rule such that the study would be stopped if first dose
# is toxic based on a Beta posterior distribution with Beta(1,1) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
target = 0.3,
prob = 0.9
)
# Evaluate if the trial will be stopped.
stopTrial(
stopping = my_stopping,
dose = dose_recommendation$value,
samples = samples,
model = model,
data = data
)
# nolint start
# Create the data
data <- DataDual(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
w = c(
0.31,
0.42,
0.59,
0.45,
0.6,
0.7,
0.55,
0.6,
0.52,
0.54,
0.56,
0.43,
0.41,
0.39,
0.34,
0.38,
0.21
),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
mean = c(0, 1),
cov = matrix(c(1, 0, 0, 1), nrow = 2),
sigma2betaW = 0.01,
sigma2W = c(a = 0.1, b = 0.1),
rho = c(a = 1, b = 1),
rw1 = TRUE
)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
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
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(
target = c(0.9, 1),
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 stopping rule such that the study would be stopped if if there is at
# least 0.5 posterior probability that the biomarker (efficacy) is within the
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker).
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1), prob = 0.5)
# Evaluate if to stop the trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
# Create some data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 50
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_nb_ncrm <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
my_dose_recommendation <- nextBest(
nextBest = my_nb_ncrm,
doselimit = 100,
samples = my_samples,
model = my_model,
data = my_data
)
# Define the stopping rules.
highest_dose_safe <- StoppingSpecificDose(
rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
dose = 80
)
max_patients <- StoppingMinPatients(nPatients = 20)
patients_near_dose <- StoppingPatientsNearDose(nPatients = 3, percentage = 0)
# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that
# the study would be stopped if 'any' of the single stopping rules is TRUE.
my_stopping <- highest_dose_safe | max_patients | patients_near_dose
# Evaluate if to stop the Trial
stopTrial(
stopping = my_stopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint start
# Create the data
data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 80, 80, 80),
y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
cohort = c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
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
)
# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
stopTarget <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
## now use the StoppingHighestDose rule:
stopHigh <-
StoppingHighestDose() &
StoppingPatientsNearDose(nPatients = 3, percentage = 0) &
StoppingTargetProb(target = c(0, 0.2), prob = 0.5)
## and combine everything:
myStopping <- stopTarget | stopHigh
# Then evaluate if to stop the trial
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
# nolint end
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data, model, options)
myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)
myNextBest <- .DefaultNextBestOrdinal()
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
myStopping <- .DefaultStoppingOrdinal()
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data, model, options)
myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)
myNextBest <- .DefaultNextBestOrdinal()
doseRecommendation <- nextBest(
myNextBest,
doselimit = nextMaxDose,
samples = samples,
model = model,
data = data
)
myStopping <- .DefaultStoppingOrdinal()
stopTrial(
stopping = myStopping,
dose = doseRecommendation$value,
samples = samples,
model = model,
data = data
)
my_rule <- StoppingExternal(report_label = "Based on combo stop")
stopTrial(
my_rule,
5,
.DefaultSamples(),
.DefaultModelLogNormal(),
.DefaultData(),
external = TRUE
)
# nolint start
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data <- Data(
x = c(25, 50, 50, 75, 150, 200, 225, 300),
y = c(0, 0, 0, 0, 1, 1, 1, 1),
doseGrid = seq(from = 25, to = 300, by = 25)
)
##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = data
)
##define MCMC options
##for illustration purpose we use 10 burn-in and generate 50 samples
options <- McmcOptions(burnin = 10, step = 2, samples = 50)
##samples of 'Samples' class
samples <- mcmc(data, model, options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTDsamples(
prob_target_drt = 0.35,
prob_target_eot = 0.3,
derive = function(samples) {
as.numeric(quantile(samples, probs = 0.3))
}
)
RecommendDose <- nextBest(
tdNextBest,
doselimit = max(data@doseGrid),
samples = samples,
model = model,
data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(
stopping = myStopping,
dose = RecommendDose$next_dose_drt,
samples = samples,
model = model,
data = data
)
# nolint end
# nolint start
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses
## we need a data object with doses >= 1:
data <- Data(
x = c(25, 50, 50, 75, 150, 200, 225, 300),
y = c(0, 0, 0, 0, 1, 1, 1, 1),
doseGrid = seq(from = 25, to = 300, by = 25)
)
##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = data
)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)
RecommendDose <- nextBest(
tdNextBest,
doselimit = max(data@doseGrid),
model = model,
data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(
stopping = myStopping,
dose = RecommendDose$next_dose_drt,
model = model,
data = data
)
# nolint end
# nolint start
##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
doseGrid = seq(25, 300, 25),
placebo = FALSE
)
##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = data
)
##Effmodel must be of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
eff = c(1.223, 2.513),
eff_dose = c(25, 300),
nu = c(a = 1, b = 0.025),
data = data
)
##for illustration purpose we use 10 burn-in and generate 50 samples
options <- McmcOptions(burnin = 10, step = 2, samples = 50)
##DLE and efficacy samples must be of 'Samples' class
DLEsamples <- mcmc(data, DLEmodel, options)
Effsamples <- mcmc(data, Effmodel, options)
##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGainSamples(
prob_target_drt = 0.35,
prob_target_eot = 0.3,
derive = function(samples) {
as.numeric(quantile(samples, prob = 0.3))
},
mg_derive = function(mg_samples) {
as.numeric(quantile(mg_samples, prob = 0.5))
}
)
RecommendDose <- nextBest(
mynextbest,
doselimit = max(data@doseGrid),
samples = DLEsamples,
model = DLEmodel,
data = data,
model_eff = Effmodel,
samples_eff = Effsamples
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(
stopping = myStopping,
dose = RecommendDose$next_dose,
samples = DLEsamples,
model = DLEmodel,
data = data,
TDderive = function(TDsamples) {
quantile(TDsamples, prob = 0.3)
},
Effmodel = Effmodel,
Effsamples = Effsamples,
Gstarderive = function(Gstarsamples) {
quantile(Gstarsamples, prob = 0.5)
}
)
# nolint end
# nolint start
##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
doseGrid = seq(25, 300, 25),
placebo = FALSE
)
##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = data
)
##Effmodel must be of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
eff = c(1.223, 2.513),
eff_dose = c(25, 300),
nu = c(a = 1, b = 0.025),
data = data
)
##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGain(prob_target_drt = 0.35, prob_target_eot = 0.3)
RecommendDose <- nextBest(
mynextbest,
doselimit = max(data@doseGrid),
model = DLEmodel,
model_eff = Effmodel,
data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(
stopping = myStopping,
dose = RecommendDose$next_dose,
model = DLEmodel,
data = data,
Effmodel = Effmodel
)
# nolint end
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