summary-PseudoDualSimulations-method: Summary for Pseudo Dual responses simulations, relative to a...

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

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Description

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Usage

## S4 method for signature 'PseudoDualSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

object	the PseudoDualSimulations object we want to summarize
trueDLE	a function which takes as input a dose (vector) and returns the true probability (vector) of DLE
trueEff	a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).
targetEndOfTrial	the target probability of DLE that are used at the end of a trial. Default at 0.3.
targetDuringTrial	the target probability of DLE that are used during the trial. Default at 0.35.
...	Additional arguments can be supplied here for trueDLE and trueEff

Value

an object of class PseudoDualSimulationsSummary

Examples

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.

# Specified simulations when no samples are used.
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 of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

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

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)

# Specify the simulations and generate the 2 trials.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Example where DLE and efficacy samples are involved.
# Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details.

# Specify the next best rule.
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))
  }
)

# Specify the design.
my_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
)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# For illustration purpose 2 simulation are created.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

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