plot-DualSimulations-missing-method: Plot dual-endpoint simulations

In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description

This plot method can be applied to DualSimulations objects in order to summarize them graphically. In addition to the standard plot types, there is

sigma2W

Plot a boxplot of the final biomarker variance estimates in the simulated trials

rho

Plot a boxplot of the final correlation estimates in the simulated trials

Usage

## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)

Arguments

x	the DualSimulations object we want to plot from
y	missing
type	the type of plots you want to obtain.
...	not used

Value

A single ggplot object if a single plot is asked for, otherwise a gridExtra{gTree} object.

Examples

# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
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")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
##Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Plot the results of the simulation
print(plot(mySims))



  

0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.