stopTrial: Stop the trial?
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description Usage Arguments Value Functions Examples

This function returns whether to stop the trial.

Stopping rule based precision of the MTD estimation. The trial is stopped, when the MTD can be estimated with sufficient precision. The criteria is based on the robust coefficient of variation (CV) calculated from the posterior distribution. The robust CV is defined mad(MTD) / median(MTD), where mad is the median absolute deviation.

Stopping based based on the lowest non placebo dose. The trial is stopped when the lowest non placebo dose meets the Hard Safety Rule, i.e. it is deemed to be overly toxic. Stopping is based on the observed data at the lowest dose level using a Bin-Beta model based on DLT probability.

Stopping based based on the lowest dose meeting the hard safety criteria using Bin-Beta model based DLT probability.

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 'StoppingMTDCV,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingLowestDoseHSRBeta,numeric,Samples,ANY,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, ...)

## S4 method for signature 'StoppingLowestDoseHSRBeta_DK,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetProbPatientsNearHighestDose,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetProbPatientsNearLowestDose,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingLowestDoseHSRBeta,numeric,Samples,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDoseBay,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHardFirst,numeric,Samples,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHardFirstFix,numeric,Samples,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingMTDdistributionBay,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDose2,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingProbBay,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

`stopping`	The rule, an object of class `Stopping`
`dose`	the recommended next best dose
`samples`	the `Samples` object
`model`	The model input, an object of class `Model`
`data`	The data input, an object of class `Data`
`...`	additional arguments
`TDderive`	the function which derives from the input, a vector of the posterior samples called `TDsamples` of the dose which has the probability of the occurrence of DLE equals to either the targetDuringTrial or targetEndOfTrial, the final next best TDtargetDuringTrial (the dose with probability of the occurrence of DLE equals to the targetDuringTrial)and TDtargetEndOfTrial estimate.
`Effmodel`	the efficacy model of `ModelEff` class object
`Effsamples`	the efficacy samples of `Samples` class object
`Gstarderive`	the function which derives from the input, a vector of the posterior Gstar (the dose which gives the maximum gain value) samples called `Gstarsamples`, the final next best Gstar estimate.

logical value: TRUE if the trial can be stopped, FALSE otherwise. It should have an attribute message which gives the reason for the decision.

stopTrial,StoppingList,ANY,ANY,ANY,ANY-method: Stop based on multiple stopping rules
stopTrial,StoppingAll,ANY,ANY,ANY,ANY-method: Stop based on fulfillment of all multiple stopping rules
stopTrial,StoppingAny,ANY,ANY,ANY,ANY-method: Stop based on fulfillment of any stopping rule
stopTrial,StoppingCohortsNearDose,numeric,ANY,ANY,Data-method: Stop based on number of cohorts near to next best dose
stopTrial,StoppingPatientsNearDose,numeric,ANY,ANY,Data-method: Stop based on number of patients near to next best dose
stopTrial,StoppingMinCohorts,ANY,ANY,ANY,Data-method: Stop based on minimum number of cohorts
stopTrial,StoppingMinPatients,ANY,ANY,ANY,Data-method: Stop based on minimum number of patients
stopTrial,StoppingTargetProb,numeric,Samples,Model,ANY-method: Stop based on probability of target tox interval
stopTrial,StoppingMTDdistribution,numeric,Samples,Model,ANY-method: Stop based on MTD distribution
stopTrial,StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY-method: Stop based on probability of targeting biomarker
stopTrial,StoppingHighestDose,numeric,ANY,ANY,Data-method: Stop when the highest dose is reached
stopTrial,StoppingTDCIRatio,ANY,Samples,ModelTox,ANY-method: Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and DLE samples are given
stopTrial,StoppingTDCIRatio,ANY,missing,ModelTox,ANY-method: Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and no DLE samples are involved
stopTrial,StoppingGstarCIRatio,ANY,Samples,ModelTox,DataDual-method: Stop based on reaching the target ratio of the upper to the lower 95% credibility 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,StoppingGstarCIRatio,ANY,missing,ModelTox,DataDual-method: Stop based on reaching the target ratio of the upper to the lower 95% credibility 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.
stopTrial,StoppingLowestDoseHSRBeta_DK,numeric,ANY,ANY,Data-method: Stop based on the lowest dose meeting the hard safety criteria using Beta based DLT probability
stopTrial,StoppingTargetProbPatientsNearHighestDose,numeric,Samples,Model,ANY-method: Stop based on probability of target tox interval and number of patients near to the highest dose
stopTrial,StoppingTargetProbPatientsNearLowestDose,numeric,Samples,Model,ANY-method: Stop based on probability of target tox interval and number of patients near to the lowest dose
stopTrial,StoppingPatientsNearDoseBay,numeric,ANY,ANY,Data-method: Stop based on number of patients near to next best dose
stopTrial,StoppingHardFirst,numeric,Samples,ANY,ANY-method: Stopping rule based on a hard stopping rule This is a method that takes into account a hardstopping rule that is not based on the model. The rule is triggered when the first dose is considered to be toxic (above threshold prob) based on the observed data and an uninformative Beta(1,1) prior (default).
stopTrial,StoppingHardFirstFix,numeric,Samples,ANY,ANY-method: Stopping rule based on hard stopping rule This method take into account the hardstopping rule, specified in the protocol. I.e. in the protocol certain numbers for stopping are specified (often not based on a posterior distribution)
stopTrial,StoppingMTDdistributionBay,numeric,Samples,Model,ANY-method: Stopping rule based on posterior dist
stopTrial,StoppingPatientsNearDose2,numeric,ANY,ANY,Data-method: Stop based on number of patients near to next best dose. Identical behavior to the original rule StoppingPatientsNearDose The only change is that additional text is putted, so that it can be identified if the rule is triggered
stopTrial,StoppingProbBay,numeric,Samples,Model,ANY-method: Stopping rule based on posterior probability

## 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




# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
                           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)

# 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),
  maxOverdoseProb = 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),
  maxOverdoseProb = 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
)

# 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)

# 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),
                           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)

# nolint end
##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

# 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)))

# 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 first dose
# is toxic based on a Beta posterior distribution with Beta(1,1) prior
myStopping <- StoppingLowestDoseHSRBeta(target = 0.3,
                                        prob = 0.9)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          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),
             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),
                           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)

# 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),
                           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)

# nolint end