DualResponsesSamplesDesign-class | R Documentation |
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.
DualResponsesSamplesDesign(eff_model, data, ...)
.DefaultDualResponsesSamplesDesign()
eff_model |
( |
data |
( |
... |
Arguments passed on to
|
data
(DataDual
)
the data set.
eff_model
(ModelEff
)
the pseudo efficacy model to be used.
Typically, end users will not use the .DefaultDualResponsesSamplesDesign()
function.
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
)
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