plot,DualSimulationsSummary,missing-method {crmPack} | R Documentation |
Plot summaries of the dual-endpoint design simulations
Description
This plot method can be applied to DualSimulationsSummary
objects in order to summarize them graphically. Possible type
of
plots at the moment are those listed in
plot,SimulationsSummary,missing-method
plus:
- meanBiomarkerFit
Plot showing the average fitted dose-biomarker curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the
trueBiomarker
argument tosummary,DualSimulations-method
)
You can specify any subset of these in the type
argument.
Usage
## S4 method for signature 'DualSimulationsSummary,missing'
plot(
x,
y,
type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit",
"meanBiomarkerFit"),
...
)
Arguments
x |
the |
y |
missing |
type |
the types of plots you want to obtain. |
... |
not used |
Value
A single ggplot
object if a single plot is
asked for, otherwise a gtable
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)
# only 10 patients here for illustration!
myStopping <- myStopping4 | StoppingMinPatients(10)
# 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
##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 summary of the Simulations
plot(summary(mySims,
trueTox = trueTox,
trueBiomarker = trueBiomarker))
[Package crmPack version 1.0.6 Index]