nextBest: Find the next best dose
In crmPack: Object-Oriented Implementation of CRM Designs
nextBest R Documentation
Find the next best dose
Description
Compute the recommended next best dose.
Usage
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestMTD,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,DataParts'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, model, data, SIM = FALSE, ...)
## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(nextBest, doselimit, model, data, Effmodel, SIM = FALSE, ...)
## S4 method for signature
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
nextBest,
doselimit,
samples,
model,
data,
Effmodel,
Effsamples,
SIM = FALSE,
...
)
Arguments
nextBest
The rule, an object of class NextBest
doselimit
The maximum allowed next dose. If this is an empty (length
0) vector, then no dose limit will be applied in the course of dose
recommendation calculation, and a corresponding warning is given.
samples
the Samples object
model
The model input, an object of class Model
data
The data input, an object of class Data
...
possible additional arguments without method dispatch
SIM
internal command to notify if this method is used within simulations. Default as FALSE
Effmodel
the efficacy model of ModelEff class object
Effsamples
the efficacy samples of Samples class object
Details
This function outputs the next best dose recommendation based on the
corresponding rule nextBest, the posterior samples from the
model and the underlying data.
Value
a list with the next best dose (element value)
on the grid defined in data, and a plot depicting this recommendation
(element plot). In case of multiple plots also an element singlePlots
is included which returns the list of single plots, which allows for further
customization of these. Also additional list elements describing the outcome
of the rule can be contained.
Functions
-
nextBest(
nextBest = NextBestMTD,
doselimit = numeric,
samples = Samples,
model = Model,
data = Data
): Find the next best dose based on the MTD rule
-
nextBest(
nextBest = NextBestNCRM,
doselimit = numeric,
samples = Samples,
model = Model,
data = Data
): Find the next best dose based on the NCRM method. The
additional list element probs contains the target and overdosing
probabilities (across all doses in the dose grid)
used in the derivation of the next best dose.
-
nextBest(
nextBest = NextBestNCRM,
doselimit = numeric,
samples = Samples,
model = Model,
data = DataParts
): Find the next best dose based on the NCRM method when
two parts trial is used - todo: need an example here for DataParts
-
nextBest(
nextBest = NextBestThreePlusThree,
doselimit = missing,
samples = missing,
model = missing,
data = Data
): Find the next best dose based on the 3+3 method
-
nextBest(
nextBest = NextBestDualEndpoint,
doselimit = numeric,
samples = Samples,
model = DualEndpoint,
data = Data
): Find the next best dose based on the dual endpoint
model. The additional list element probs contains the target and
overdosing probabilities (across all doses in the dose grid) used in the
derivation of the next best dose.
-
nextBest(
nextBest = NextBestTDsamples,
doselimit = numeric,
samples = Samples,
model = LogisticIndepBeta,
data = Data
): Find the next best dose based on the 'NextBestTDsamples'
class rule. This a method based only on the DLE responses and for
LogisticIndepBeta model class object involving DLE samples
-
nextBest(
nextBest = NextBestTD,
doselimit = numeric,
samples = missing,
model = LogisticIndepBeta,
data = Data
): Find the next best dose based on the 'NextBestTD'
class rule. This a method based only on the DLE responses and for
LogisticIndepBeta model class object without DLE samples
-
nextBest(
nextBest = NextBestMaxGain,
doselimit = numeric,
samples = missing,
model = ModelTox,
data = DataDual
): for slots nextBest,doselimit, data and SIM. This is
a function to find the next best dose based on the 'NextBestMaxGain'
class rule. This a method based on the DLE responses and efficacy responses without DLE and
efficacy samples.
-
nextBest(
nextBest = NextBestMaxGainSamples,
doselimit = numeric,
samples = Samples,
model = ModelTox,
data = DataDual
): for slots nextBest,doselimit, data and SIM. This is
a function to find the next best dose based on the 'NextBestMaxGainSamples'
class rule. This a method based on the DLE responses and efficacy responses with DLE and
efficacy samples. Effmodel must be of class Effloglog or
EffFlexi.
Examples
# 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 'NextBestMTD'
mtdNextBest <- NextBestMTD(target=0.33,
derive=
function(mtdSamples){
quantile(mtdSamples, probs=0.25)
})
# Calculate the next best dose
doseRecommendation <- nextBest(mtdNextBest,
doselimit=nextMaxDose,
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)
# Look at the probabilities
doseRecommendation$probs
# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
y=c(0,0,0),
doseGrid=c(0.1,0.5,1.5,3,6,
seq(from=10,to=80,by=2)),
part=c(1L,1L,1L),
nextPart=1L,
part1Ladder=c(0.1,0.5,1.5,3,6,10))
# 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)
myIncrements <- IncrementsRelativeParts(dltStart=0,
cleanStart=1)
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)
# Create the data
data <- Data(x=c(5, 5, 5, 10, 10, 10),
y=c(0, 0, 0, 0, 1, 0),
cohort=c(0, 0, 0, 1, 1, 1),
doseGrid=
c(0.1, 0.5, 1.5, 3, 5,
seq(from=10, to=80, by=2)))
# The rule to select the next best dose will be based on the 3+3 method
myNextBest <- NextBestThreePlusThree()
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
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)
## joint plot
print(doseRecommendation$plot)
## show customization of single plot
variant1 <- doseRecommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)
## 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))
##The 'nextBest' method using NextBestTDsamples' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)
##Then genreate the samples
samples <- mcmc(data, model, options)
##target probabilities of the occurrence of a DLE during trial and at the end of trial are
## defined as 0.35 and 0.3, respectively
##Specified in 'derive' such that the 30% posterior quantile of the TD35 and TD30 samples
## will be used as TD35 and TD30 estimates
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
model=model,data=data)
## 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))
##The 'nextBest' method using NextBestTD' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##target probabilities of the occurrence of a DLE during trial and at the end of trial
## are defined as 0.35 and 0.3, respectively
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<- nextBest(tdNextBest,
doselimit=max(data@doseGrid),
model=model,
data=data)
## 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)
##The 'nextBest' method using NextBestMaxGain' rules class object
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## using the 'ModelEff' class efficacy model
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as
## 0.35 and 0.3, respectively
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(mynextbest,doselimit=300,model=DLEmodel,Effmodel=Effmodel,data=data)
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)
##The 'nextBest' method using NextBestMaxGainSamples' rules class object
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## using the 'ModelEff' class efficacy model
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)
##target probabilities of the occurrence of a DLE during trial and at the end of trial
## are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35 and TD30,
## function specified in TDderive slot
## Using the 50% posterior quantile of the Gstar (the dose which gives the maxim gain value)
## samples as Gstar estimate,function specified in Gstarderive slot
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)
## now using the 'EffFlexi' class efficacy model:
##The 'nextBest' method using NextBestMaxGainSamples' rules class object for 'EffFlexi' model class
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
sigma2=c(a=0.1,b=0.1),
sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)
##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of
## TD35 and TD30, function specified in TDderive slot
## Using the 50% posterio quantile of the Gstar (the dose which gives the maximum
## gain value) samples as Gstar estimate,function specified in Gstarderive slot
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)
crmPack documentation built on June 26, 2024, 5:07 p.m.
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| nextBest | R Documentation |
Find the next best dose
Description
Compute the recommended next best dose.
Usage
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestMTD,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,DataParts'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)
## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, model, data, SIM = FALSE, ...)
## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(nextBest, doselimit, model, data, Effmodel, SIM = FALSE, ...)
## S4 method for signature
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
nextBest,
doselimit,
samples,
model,
data,
Effmodel,
Effsamples,
SIM = FALSE,
...
)
Arguments
nextBest |
The rule, an object of class |
doselimit |
The maximum allowed next dose. If this is an empty (length 0) vector, then no dose limit will be applied in the course of dose recommendation calculation, and a corresponding warning is given. |
samples |
the |
model |
The model input, an object of class |
data |
The data input, an object of class |
... |
possible additional arguments without method dispatch |
SIM |
internal command to notify if this method is used within simulations. Default as FALSE |
Effmodel |
the efficacy model of |
Effsamples |
the efficacy samples of |
Details
This function outputs the next best dose recommendation based on the
corresponding rule nextBest, the posterior samples from the
model and the underlying data.
Value
a list with the next best dose (element value)
on the grid defined in data, and a plot depicting this recommendation
(element plot). In case of multiple plots also an element singlePlots
is included which returns the list of single plots, which allows for further
customization of these. Also additional list elements describing the outcome
of the rule can be contained.
Functions
-
nextBest( nextBest = NextBestMTD, doselimit = numeric, samples = Samples, model = Model, data = Data ): Find the next best dose based on the MTD rule -
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = Model, data = Data ): Find the next best dose based on the NCRM method. The additional list elementprobscontains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose. -
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = Model, data = DataParts ): Find the next best dose based on the NCRM method when two parts trial is used - todo: need an example here for DataParts -
nextBest( nextBest = NextBestThreePlusThree, doselimit = missing, samples = missing, model = missing, data = Data ): Find the next best dose based on the 3+3 method -
nextBest( nextBest = NextBestDualEndpoint, doselimit = numeric, samples = Samples, model = DualEndpoint, data = Data ): Find the next best dose based on the dual endpoint model. The additional list elementprobscontains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose. -
nextBest( nextBest = NextBestTDsamples, doselimit = numeric, samples = Samples, model = LogisticIndepBeta, data = Data ): Find the next best dose based on the 'NextBestTDsamples' class rule. This a method based only on the DLE responses and forLogisticIndepBetamodel class object involving DLE samples -
nextBest( nextBest = NextBestTD, doselimit = numeric, samples = missing, model = LogisticIndepBeta, data = Data ): Find the next best dose based on the 'NextBestTD' class rule. This a method based only on the DLE responses and forLogisticIndepBetamodel class object without DLE samples -
nextBest( nextBest = NextBestMaxGain, doselimit = numeric, samples = missing, model = ModelTox, data = DataDual ): for slotsnextBest,doselimit,dataandSIM. This is a function to find the next best dose based on the 'NextBestMaxGain' class rule. This a method based on the DLE responses and efficacy responses without DLE and efficacy samples. -
nextBest( nextBest = NextBestMaxGainSamples, doselimit = numeric, samples = Samples, model = ModelTox, data = DataDual ): for slotsnextBest,doselimit,dataandSIM. This is a function to find the next best dose based on the 'NextBestMaxGainSamples' class rule. This a method based on the DLE responses and efficacy responses with DLE and efficacy samples. Effmodel must be of classEffloglogorEffFlexi.
Examples
# 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 'NextBestMTD'
mtdNextBest <- NextBestMTD(target=0.33,
derive=
function(mtdSamples){
quantile(mtdSamples, probs=0.25)
})
# Calculate the next best dose
doseRecommendation <- nextBest(mtdNextBest,
doselimit=nextMaxDose,
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)
# Look at the probabilities
doseRecommendation$probs
# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
y=c(0,0,0),
doseGrid=c(0.1,0.5,1.5,3,6,
seq(from=10,to=80,by=2)),
part=c(1L,1L,1L),
nextPart=1L,
part1Ladder=c(0.1,0.5,1.5,3,6,10))
# 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)
myIncrements <- IncrementsRelativeParts(dltStart=0,
cleanStart=1)
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)
# Create the data
data <- Data(x=c(5, 5, 5, 10, 10, 10),
y=c(0, 0, 0, 0, 1, 0),
cohort=c(0, 0, 0, 1, 1, 1),
doseGrid=
c(0.1, 0.5, 1.5, 3, 5,
seq(from=10, to=80, by=2)))
# The rule to select the next best dose will be based on the 3+3 method
myNextBest <- NextBestThreePlusThree()
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
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)
## joint plot
print(doseRecommendation$plot)
## show customization of single plot
variant1 <- doseRecommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)
## 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))
##The 'nextBest' method using NextBestTDsamples' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)
##Then genreate the samples
samples <- mcmc(data, model, options)
##target probabilities of the occurrence of a DLE during trial and at the end of trial are
## defined as 0.35 and 0.3, respectively
##Specified in 'derive' such that the 30% posterior quantile of the TD35 and TD30 samples
## will be used as TD35 and TD30 estimates
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
model=model,data=data)
## 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))
##The 'nextBest' method using NextBestTD' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##target probabilities of the occurrence of a DLE during trial and at the end of trial
## are defined as 0.35 and 0.3, respectively
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<- nextBest(tdNextBest,
doselimit=max(data@doseGrid),
model=model,
data=data)
## 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)
##The 'nextBest' method using NextBestMaxGain' rules class object
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## using the 'ModelEff' class efficacy model
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as
## 0.35 and 0.3, respectively
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)
##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(mynextbest,doselimit=300,model=DLEmodel,Effmodel=Effmodel,data=data)
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)
##The 'nextBest' method using NextBestMaxGainSamples' rules class object
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## using the 'ModelEff' class efficacy model
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)
##target probabilities of the occurrence of a DLE during trial and at the end of trial
## are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35 and TD30,
## function specified in TDderive slot
## Using the 50% posterior quantile of the Gstar (the dose which gives the maxim gain value)
## samples as Gstar estimate,function specified in Gstarderive slot
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)
## now using the 'EffFlexi' class efficacy model:
##The 'nextBest' method using NextBestMaxGainSamples' rules class object for 'EffFlexi' model class
## using the 'ModelTox' class DLE model
## DLEmodel e.g 'LogisticIndepBeta' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
sigma2=c(a=0.1,b=0.1),
sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)
##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of
## TD35 and TD30, function specified in TDderive slot
## Using the 50% posterio quantile of the Gstar (the dose which gives the maximum
## gain value) samples as Gstar estimate,function specified in Gstarderive slot
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)
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