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

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

Plot dual-endpoint simulations

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))

# Create some data.
my_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
  ),
  ID = 1:17,
  cohort = c(1L, 2L, 3L, 4L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L),
  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 CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = 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),
  rw1 = TRUE
)

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

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

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping
my_stopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
my_stopping <- my_stopping4 | StoppingMinPatients(40) | StoppingMissingDose()

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

# Initialize the design

my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- 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
}

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

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

# Draw the TRUE profiles
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(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.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 9,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 1,
    step = 1,
    samples = 2
  )
)

# Plot the results of the simulation.
print(plot(my_sims))

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.