This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory

Summary of the trajectory of the simulated trials

dosesTried

Average proportions of the doses tested in patients

sigma2

The variance of the efficacy responses

sigma2betaW

The variance of the random walk model

You can specify one or both of these in the type argument.

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory

Summary of the trajectory of the simulated trials

dosesTried

Average proportions of the doses tested in patients

sigma2

The variance of the efficacy responses

sigma2betaW

The variance of the random walk model

You can specify one or both of these in the type argument.

Usage

# S4 method for class 'PseudoDualFlexiSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)

Arguments

x

the PseudoDualFlexiSimulations 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

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model must be of 'EffFlexi' class.
eff_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The cohort size, size of 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Define the stopping rule. Stop when the maximum sample size of 36 patients has
# been reached or when the next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design.
design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)
# Specify the true DLE curve and the true expected efficacy values
# at all dose levels.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)

my_truth_eff <- c(
  -0.5478867, 0.1645417, 0.5248031, 0.7604467,
  0.9333009, 1.0687031, 1.1793942, 1.2726408,
  1.3529598, 1.4233411, 1.4858613, 1.5420182
)

# The true gain curve.
my_truth_gain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

# MCMC options.
my_options <- McmcOptions(burnin = 10, step = 1, samples = 20)

# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = my_options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Plot the simulated results.
print(plot(my_sim))