Description
![[Experimental]](figures/lifecycle-experimental.svg?package=crmPack&version=2.2.1)
Usage
Value
Arguments
Functions
Examples
Run this code# nolint start
# Define the dose-grid and a hypothetical observed data scenario.
data <- Data(
x = c(1, 3, 3, 5, 5, 5),
y = c(0, 0, 0, 0, 1, 0),
cohort = c(1, 2, 2, 3, 3, 3),
doseGrid = c(1, 3, 5, 10, 15, 20, 25)
)
# Initialize the CRM model.
model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Choose the rule for selecting the next dose.
next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Choose the rule for stopping.
stopping <- StoppingMinPatients(nPatients = 20) | StoppingMissingDose()
# Choose the rule for dose increments.
increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
# Initialize the design.
design <- Design(
model = model,
nextBest = next_best,
stopping = stopping,
increments = increments,
cohort_size = CohortSizeConst(3),
data = Data(doseGrid = data@doseGrid), # empty data here.
startingDose = 1
)
options <- McmcOptions(
burnin = 10,
step = 1,
samples = 20,
rng_kind = "Super-Duper",
rng_seed = 94
)
# \donttest{
result <- scenario(design, data, options)
result$fit
result$next_dose
result$cohort_size
result$stop
# }
# nolint end
# nolint start
# Define a hypothetical two-drug scenario.
data <- DataCombo(
x = cbind(
drug1 = c(10, 10, 10, 20, 20, 20),
drug2 = c(20, 20, 20, 20, 20, 20)
),
y = c(0, 0, 1, 0, 0, 0),
doseGrid = list(drug1 = c(10, 20, 30), drug2 = c(20, 40, 60))
)
model <- TwoDrugsCombo(
single_models = list(
drug1 = LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 10
),
drug2 = LogisticLogNormal(
mean = c(-0.7, 0.8),
cov = matrix(c(1.1, -0.3, -0.3, 0.9), nrow = 2),
ref_dose = 20
)
),
gamma = 0,
tau = 1
)
increments <- IncrementsMin(
increments_list = list(
IncrementsComboOneDrugOnly(),
IncrementsComboCartesian(
drug1 = IncrementsRelative(intervals = c(0), increments = c(1)),
drug2 = IncrementsRelative(intervals = c(0), increments = c(1))
)
)
)
design <- DesignCombo(
model = model,
nextBest = NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
),
stopping = StoppingMinPatients(nPatients = 20),
increments = increments,
cohort_size = CohortSizeConst(3),
data = DataCombo(doseGrid = data@doseGrid),
startingDose = c(drug1 = 10, drug2 = 20)
)
options <- McmcOptions(
burnin = 10,
step = 1,
samples = 20,
rng_kind = "Super-Duper",
rng_seed = 94
)
# \donttest{
result <- scenario(design, data, options)
result$fit
result$next_dose
result$cohort_size
result$stop
# }
# nolint end
# nolint start
# Define a hypothetical time-to-DLT scenario.
data <- DataDA(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 1, 1, 0, 0, 1, 0),
u = c(42, 30, 15, 5, 20, 25, 30, 60),
t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
Tmax = 60,
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
ID = 1L:8L,
cohort = as.integer(c(1, 2, 3, 4, 5, 6, 6, 6))
)
npiece <- 10
t_max <- 60
lambda_prior <- function(k) {
npiece / (t_max * (npiece - k + 0.5))
}
model <- DALogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56,
npiece = npiece,
l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
c_par = 2
)
size1 <- CohortSizeRange(
intervals = c(0, 30),
cohort_size = c(1, 3)
)
size2 <- CohortSizeDLT(
intervals = c(0, 1),
cohort_size = c(1, 3)
)
design <- DADesign(
model = model,
increments = IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
),
nextBest = NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
),
stopping = StoppingTargetProb(
target = c(0.2, 0.35),
prob = 0.5
) | StoppingMinPatients(nPatients = 50) | StoppingMissingDose(),
cohort_size = maxSize(size1, size2),
data = DataDA(doseGrid = data@doseGrid, Tmax = data@Tmax),
safetyWindow = SafetyWindowConst(c(6, 2), 7, 7),
startingDose = 3
)
options <- McmcOptions(
burnin = 10,
step = 1,
samples = 20,
rng_kind = "Super-Duper",
rng_seed = 94
)
# \donttest{
result <- scenario(design, data, options)
result$fit
result$next_dose
result$cohort_size
result$stop
# }
# nolint end
# nolint start
dose_grid <- c(1, 3, 5, 10, 15, 20, 25)
# Define hypothetical observed data for two related arms.
data <- HierarchicalData(
arm_a = Data(
x = c(1, 3, 3, 5),
y = c(0, 0, 0, 1),
cohort = c(1, 2, 2, 3),
doseGrid = dose_grid
),
arm_b = Data(
x = c(1, 1, 3, 3),
y = c(0, 0, 0, 0),
cohort = c(1, 1, 2, 2),
doseGrid = dose_grid
)
)
model_a <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 10
)
model_b <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 10
)
next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
stopping <- StoppingMinPatients(nPatients = 20) | StoppingMissingDose()
increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
design_a <- Design(
model = model_a,
nextBest = next_best,
stopping = stopping,
increments = increments,
cohort_size = CohortSizeConst(3),
data = Data(doseGrid = dose_grid),
startingDose = 1
)
design_b <- Design(
model = model_b,
nextBest = next_best,
stopping = stopping,
increments = increments,
cohort_size = CohortSizeConst(3),
data = Data(doseGrid = dose_grid),
startingDose = 1
)
design <- HierarchicalDesign(
DesignArm(
name = "arm_a",
design = design_a
),
DesignArm(
name = "arm_b",
design = design_b
),
exchangeable_parameters = list(
intercept = list(
arm_a = "alpha0",
arm_b = "alpha0"
),
slope = list(
arm_a = "alpha1",
arm_b = "alpha1"
)
)
)
options <- McmcOptions(
burnin = 10,
step = 1,
samples = 20,
rng_kind = "Super-Duper",
rng_seed = 94
)
# \donttest{
result <- scenario(design, data, options)
result$fit
result$next_dose
result$cohort_size
result$stop
# }
# nolint end
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