maxDose: Determine the maximum possible next dose

In crmPack: Object-Oriented Implementation of CRM Designs

Determine the maximum possible next dose

Description

Determine the upper limit of the next dose based on the increments rule.

Usage

maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelative,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsNumDoseLevels,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeParts,DataParts'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLT,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementMin,Data'
maxDose(increments, data, ...)

Arguments

increments	The rule, an object of class Increments
data	The data input, an object of class Data
...	further arguments

Details

This function outputs the maximum possible next dose, based on the corresponding rule increments and the data.

Value

the maximum possible next dose

Functions

• maxDose(increments = IncrementsRelative, data = Data): Determine the maximum possible next dose based on relative increments

• maxDose(increments = IncrementsNumDoseLevels, data = Data): Determine the maximum possible next dose based on maximum dose levels to increment for the next dose

• maxDose(increments = IncrementsRelativeParts, data = DataParts): Determine the maximum possible next dose based on relative increments and part 1 and 2

• maxDose(increments = IncrementsRelativeDLT, data = Data): Determine the maximum possible next dose based on relative increments determined by DLTs so far

• maxDose(increments = IncrementMin, data = Data): Determine the maximum possible next dose based on multiple increment rules (taking the minimum across individual increments).

Examples

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             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, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if the last dose was below 20
#   - only increasing the dose by 1.33 if the last dose was equal or above 20
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             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, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
# maximum skip one dose level, that is 2 dose levels higher is maximum
# increment
myIncrements <- IncrementsNumDoseLevels(maxLevels=2)

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# create an object of class 'DataParts'
myData <- 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))


myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)

nextMaxDose <- maxDose(myIncrements,
                       data=myData)


# 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)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if no DLTs were yet observed
#   - only increasing the dose by 1.33 if 1 or 2 DLTs were already observed
#   - only increasing the dose by 1.2 if at least 3 DLTs were already observed
myIncrements <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             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, 8,
                 seq(from=10, to=80, by=2)))


# As example, here we are combining 2 different increment rules. 

# The first rule is the following: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

# The second rule is the following: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20

myIncrements1 <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                       increments = c(1, 0.33, 0.2))

myIncrements2 <- IncrementsRelative(intervals=c(0, 20),
                                    increments=c(1, 0.33))

# Now we combine the 2 rules
combIncrement <- IncrementMin(IncrementsList=
                                list(myIncrements1,myIncrements2))

# Finally we then calculate the maximum dose allowed by taking the minimum of the two rules
nextMaxDose <- maxDose(combIncrement, 
                       data)

crmPack documentation built on June 26, 2024, 5:07 p.m.