simulate-DesignGrouped-method: Simulate Method for the 'DesignGrouped' Class

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

Simulate Method for the DesignGrouped Class

Description

A simulate method for DesignGrouped designs.

Usage

## S4 method for signature 'DesignGrouped'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  combo_truth,
  args = data.frame(),
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallelly::availableCores(), 5),
  ...
)

Arguments

object	(DesignGrouped) the design we want to simulate trials from.
nsim	(number) how many trials should be simulated.
seed	(RNGstate) generated with set_seed().
truth	(function) a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity for the mono arm. Additional arguments can be supplied in args.
combo_truth	(function) same as truth but for the combo arm.
args	(data.frame) optional data.frame with arguments that work for both the truth and combo_truth functions. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.
firstSeparate	(flag) whether to enroll the first patient separately from the rest of the cohort and close the cohort in case a DLT occurs in this first patient.
mcmcOptions	(McmcOptions) MCMC options for each evaluation in the trial.
parallel	(flag) whether the simulation runs are parallelized across the cores of the computer.
nCores	(number) how many cores should be used for parallel computing.
...	not used.

Value

A list of mono and combo simulation results as Simulations objects.

Examples

# Assemble ingredients for our group design.
my_stopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5) |
  StoppingMinPatients(10) |
  StoppingMissingDose()
my_increments <- IncrementsDoseLevels(levels = 3L)
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.3),
  overdose = c(0.3, 1),
  max_overdose_prob = 0.3
)
my_cohort_size <- CohortSizeConst(3)
empty_data <- Data(doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)))
my_model <- LogisticLogNormalGrouped(
  mean = c(-4, -4, -4, -4),
  cov = diag(rep(6, 4)),
  ref_dose = 0.1
)

# Put together the design. Note that if we only specify the mono arm,
# then the combo arm is having the same settings.
my_design <- DesignGrouped(
  model = my_model,
  mono = Design(
    model = my_model,
    stopping = my_stopping,
    increments = my_increments,
    nextBest = my_next_best,
    cohort_size = my_cohort_size,
    data = empty_data,
    startingDose = 0.1
  ),
  first_cohort_mono_only = TRUE,
  same_dose_for_all = TRUE
)

# Set up a realistic simulation scenario.
my_truth <- function(x) plogis(-4 + 0.2 * log(x / 0.1))
my_combo_truth <- function(x) plogis(-4 + 0.5 * log(x / 0.1))
matplot(
  x = empty_data@doseGrid,
  y = cbind(
    mono = my_truth(empty_data@doseGrid),
    combo = my_combo_truth(empty_data@doseGrid)
  ),
  type = "l",
  ylab = "true DLT prob",
  xlab = "dose"
)
legend("topright", c("mono", "combo"), lty = c(1, 2), col = c(1, 2))

# Start the simulations.
set.seed(123)
my_sims <- simulate(
  my_design,
  nsim = 1, # This should be at least 100 in actual applications.
  seed = 123,
  truth = my_truth,
  combo_truth = my_combo_truth
)

# Looking at the summary of the simulations:
mono_sims_sum <- summary(my_sims$mono, truth = my_truth)
combo_sims_sum <- summary(my_sims$combo, truth = my_combo_truth)

mono_sims_sum
combo_sims_sum

plot(mono_sims_sum)
plot(combo_sims_sum)

# Looking at specific simulated trials:
trial_index <- 1
plot(my_sims$mono@data[[trial_index]])
plot(my_sims$combo@data[[trial_index]])

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