| stopTrial {crmPack} | R Documentation |
Stop the trial?
Description
This function returns whether to stop the trial.
Usage
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingList,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingAll,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingAny,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingCohortsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingPatientsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingMinCohorts,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingMinPatients,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingTargetProb,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingMTDdistribution,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature
## 'StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingHighestDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingTDCIRatio,ANY,Samples,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingTDCIRatio,ANY,missing,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)
## S4 method for signature 'StoppingGstarCIRatio,ANY,Samples,ModelTox,DataDual'
stopTrial(
stopping,
dose,
samples,
model,
data,
TDderive,
Effmodel,
Effsamples,
Gstarderive,
...
)
## S4 method for signature 'StoppingGstarCIRatio,ANY,missing,ModelTox,DataDual'
stopTrial(stopping, dose, model, data, Effmodel, ...)
Arguments
stopping |
The rule, an object of class
|
dose |
the recommended next best dose |
samples |
the |
model |
The model input, an object of class |
data |
The data input, an object of class |
... |
additional arguments |
TDderive |
the function which derives from the input, a vector of the posterior samples called
|
Effmodel |
the efficacy model of |
Effsamples |
the efficacy samples of |
Gstarderive |
the function which derives from the input, a vector of the posterior Gstar (the dose
which gives the maximum gain value) samples
called |
Value
logical value: TRUE if the trial can be stopped, FALSE
otherwise. It should have an attribute message which gives the reason
for the decision.
Functions
-
stopTrial( stopping = StoppingList, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on multiple stopping rules -
stopTrial( stopping = StoppingAll, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of all multiple stopping rules -
stopTrial( stopping = StoppingAny, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of any stopping rule -
stopTrial( stopping = StoppingCohortsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of cohorts near to next best dose -
stopTrial( stopping = StoppingPatientsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of patients near to next best dose -
stopTrial( stopping = StoppingMinCohorts, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of cohorts -
stopTrial( stopping = StoppingMinPatients, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of patients -
stopTrial( stopping = StoppingTargetProb, dose = numeric, samples = Samples, model = Model, data = ANY ): Stop based on probability of target tox interval -
stopTrial( stopping = StoppingMTDdistribution, dose = numeric, samples = Samples, model = Model, data = ANY ): Stop based on MTD distribution -
stopTrial( stopping = StoppingTargetBiomarker, dose = numeric, samples = Samples, model = DualEndpoint, data = ANY ): Stop based on probability of targeting biomarker -
stopTrial( stopping = StoppingHighestDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop when the highest dose is reached -
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = ANY ): Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95 interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and DLE samples are given -
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = missing, model = ModelTox, data = ANY ): Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95 interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and no DLE samples are involved -
stopTrial( stopping = StoppingGstarCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95 interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporate DLE and efficacy responses and DLE and efficacy samples are also used. -
stopTrial( stopping = StoppingGstarCIRatio, dose = ANY, samples = missing, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95 interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporate DLE and efficacy responses without DLE and efficacy samples involved.
Examples
## 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
# 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),
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)
# 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(stopList=c(myStopping1,myStopping2,myStopping3),
summary=any)
# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
samples=samples, model=model, data=data)
# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y=c(0, 0, 0, 0, 0, 0, 1, 0),
cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
refDose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3
# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
samples=samples, model=model, data=data)
# 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),
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)
# 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$value,
samples=samples, model=model, data=data)
# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y=c(0, 0, 0, 0, 0, 0, 1, 0),
cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
refDose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Define the stopping rule such that the study would be stopped if at least 3
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3,
percentage = 0.2)
# Evaluate if to stop the trial
stopTrial(stopping=myStopping,
dose=doseRecommendation$value,
data=data)
# 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)
# 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$value,
data=data)
# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y=c(0, 0, 0, 0, 0, 0, 1, 0),
cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
refDose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Define the stopping rule such that the study would be stopped if at least 6
# cohorts were already dosed
myStopping <- StoppingMinCohorts(nCohorts = 6)
# Evaluate if to stop the trial
stopTrial(stopping=myStopping,
dose=doseRecommendation$value,
data=data)
# 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)
# 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$value,
data=data)
# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y=c(0, 0, 0, 0, 0, 0, 1, 0),
cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
refDose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
myStopping <- StoppingTargetProb(target=c(0.2, 0.35),
prob=0.5)
# Evaluate if to stop the trial
stopTrial(stopping=myStopping,
dose=doseRecommendation$value,
samples=samples,
model=model,
data=data)
# 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)
# 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$value,
samples=samples,
model=model,
data=data)
# Create the data
data <- DataDual(
x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
20, 20, 20, 40, 40, 40, 50, 50, 50),
y=c(0, 0, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 0, 1, 0, 1, 1),
w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
doseGrid=c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
Sigma = matrix(c(1, 0, 0, 1), nrow=2),
sigma2betaW = 0.01,
sigma2W = c(a=0.1, b=0.1),
rho = c(a=1, b=1),
smooth = "RW1")
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=500)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples,
model=model,
data=data)
# Define the stopping rule such that the study would be stopped if if there is at
# least 0.5 posterior probability that the biomarker (efficacy) is within the
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker).
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1),
prob = 0.5)
# Evaluate if to stop the trial
stopTrial(stopping=myStopping,
dose=doseRecommendation$value,
samples=samples,
model=model,
data=data)
# 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),
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)
# 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$value,
samples=samples,
model=model,
data=data)
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
y=c(0,0,0,0,1,1,1,1),
doseGrid=seq(from=25,to=300,by=25))
##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define MCMC options
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##samples of 'Samples' class
samples<-mcmc(data,model,options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
samples=samples,model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
y=c(0,0,0,0,1,1,1,1),
doseGrid=seq(from=25,to=300,by=25))
##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
y=c(0,0,0,0,0,1,1,0),
w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
doseGrid=seq(25,300,25),
placebo=FALSE)
##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Effmodel must be of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##DLE and efficacy samples must be of 'Samples' class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)
##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
DLEEndOfTrialtarget=0.3,
TDderive=function(TDsamples){
quantile(TDsamples,prob=0.3)},
Gstarderive=function(Gstarsamples){
quantile(Gstarsamples,prob=0.5)})
RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,model=DLEmodel,
data=data,Effmodel=Effmodel,Effsamples=Effsamples)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(stopping=myStopping,
dose=RecommendDose$nextdose,
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)})
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
y=c(0,0,0,0,0,1,1,0),
w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
doseGrid=seq(25,300,25),
placebo=FALSE)
##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Effmodel must be of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)
RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),model=DLEmodel,
Effmodel=Effmodel,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,model=DLEmodel,
data=data, Effmodel=Effmodel)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
[Package crmPack version 1.0.6 Index]