plot-DualSimulationsSummary-missing-method: Plot summaries of the dual-endpoint design simulations

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

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 to summary,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 DualSimulationsSummary object we want to plot from
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 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.
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.
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
)
# Only 10 patients here for illustration!
my_stopping <- my_stopping4 | StoppingMinPatients(10) | 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.
# 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 = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Plot the summary of the Simulations.
plot(summary(my_sims,
  trueTox = true_tox,
  trueBiomarker = true_biomarker
))

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