DualResponsesSamplesDesign

This is a class of design based on DLE responses using the LogisticIndepBeta model with DLE and efficacy samples. It contain all slots in RuleDesign and TDsamplesDesign class objects.

Usage

DualResponsesSamplesDesign(eff_model, data, ...)

.DefaultDualResponsesSamplesDesign()

Arguments

eff_model

(ModelEff)
see slot definition.

data

(DataDual)
see slot definition.

...

Arguments passed on to TDsamplesDesign

model

(ModelTox)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

Slots

data

(DataDual)
the data set.

eff_model

(ModelEff)
the pseudo efficacy model to be used.

Note

Typically, end users will not use the .DefaultDualResponsesSamplesDesign() function.

Examples

empty_data <- DataDual(doseGrid = seq(25, 300, 25))

tox_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
tox_samples <- mcmc(empty_data, tox_model, options)

eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data
)
eff_samples <- mcmc(empty_data, eff_model, options)

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))
  }
)

my_increments <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
my_size <- CohortSizeConst(size = 3)
my_stopping <- StoppingMinPatients(nPatients = 36)

design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = tox_model,
  eff_model = eff_model,
  data = empty_data,
  stopping = my_stopping,
  increments = my_increments
)