Description
![[Stable]](figures/lifecycle-stable.svg?package=crmPack&version=2.0.0)
Usage
Value
Arguments
Functions
Examples
Run this code# Example of usage for `NextBestEWOC` NextBest class.
# Create the data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestEWOC' class.
ewoc_next_best <- NextBestEWOC(
target = 0.30,
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = ewoc_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestMTD` NextBest class.
# Create the data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestMTD' class.
mtd_next_best <- NextBestMTD(
target = 0.33,
derive = function(mtd_samples) {
quantile(mtd_samples, probs = 0.25)
}
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = mtd_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Example of usage for `NextBestNCRM` NextBest class.
# Create the data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = nrcm_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestNCRM-DataParts` NextBest class.
# Create the data.
my_data <- DataParts(
x = c(0.1, 0.5, 1.5),
y = c(0, 0, 0),
ID = 1:3,
cohort = 1:3,
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
part = c(1L, 1L, 1L),
nextPart = 1L,
part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelativeParts(
dlt_start = 0,
clean_start = 1
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = nrcm_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
dose_recommendation
# Example of usage for `NextBestNCRMLoss` NextBest class.
# Create the data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the class `NextBestNCRMLoss`.
nrcm_loss_next_best <- NextBestNCRMLoss(
target = c(0.2, 0.35),
overdose = c(0.35, 0.6),
unacceptable = c(0.6, 1),
max_overdose_prob = 0.999,
losses = c(1, 0, 1, 2)
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = nrcm_loss_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Next best dose.
dose_recommendation$value
# Look at the probabilities.
dose_recommendation$probs
# Define another rule (loss function of 3 elements).
nrcm_loss_next_best_losses_3 <- NextBestNCRMLoss(
target = c(0.2, 0.35),
overdose = c(0.35, 1),
max_overdose_prob = 0.30,
losses = c(1, 0, 2)
)
# Calculate the next best dose.
dose_recommendation_losses_3 <- nextBest(
nextBest = nrcm_loss_next_best_losses_3,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Next best dose.
dose_recommendation_losses_3$value
# Look at the probabilities.
dose_recommendation_losses_3$probs
# Example of usage for `NextBestThreePlusThree` NextBest class.
# Create the data.
my_data <- Data(
x = c(5, 5, 5, 10, 10, 10),
y = c(0, 0, 0, 0, 1, 0),
ID = 1:6,
cohort = c(0, 0, 0, 1, 1, 1),
doseGrid = c(0.1, 0.5, 1.5, 3, 5, seq(from = 10, to = 80, by = 2))
)
# The rule to select the next best dose will be based on the 3+3 method.
my_next_best <- NextBestThreePlusThree()
# Calculate the next best dose.
dose_recommendation <- nextBest(my_next_best, data = my_data)
# Example of usage for `NextBestDualEndpoint` NextBest class.
# Create the data.
my_data <- DataDual(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
ID = 1:17,
cohort = c(
1L,
2L,
3L,
4L,
5L,
6L,
6L,
6L,
7L,
7L,
7L,
8L,
8L,
8L,
9L,
9L,
9L
),
w = c(
0.31,
0.42,
0.59,
0.45,
0.6,
0.7,
0.55,
0.6,
0.52,
0.54,
0.56,
0.43,
0.41,
0.39,
0.34,
0.38,
0.21
),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
mean = c(0, 1),
cov = matrix(c(1, 0, 0, 1), nrow = 2),
sigma2betaW = 0.01,
sigma2W = c(a = 0.1, b = 0.1),
rho = c(a = 1, b = 1),
rw1 = TRUE
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose. In this case,
# target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety).
de_next_best <- NextBestDualEndpoint(
target = c(0.9, 1),
overdose = c(0.35, 1),
max_overdose_prob = 0.25
)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = de_next_best,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# See the probabilities.
dose_recommendation$probs
# Joint plot.
print(dose_recommendation$plot)
# Show customization of single plot.
variant1 <- dose_recommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)
# Example of usage for `NextBestTD` NextBest class.
my_data <- Data(
x = c(25, 50, 50, 75, 150, 200, 225, 300),
y = c(0, 0, 0, 0, 1, 1, 1, 1),
ID = 1:8,
cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
doseGrid = seq(from = 25, to = 300, by = 25)
)
my_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = my_data
)
# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
td_next_best <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)
# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
nextBest = td_next_best,
doselimit = max(my_data@doseGrid),
model = my_model,
data = my_data
)
dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestTDsamples` NextBest class.
my_data <- Data(
x = c(25, 50, 50, 75, 150, 200, 225, 300),
y = c(0, 0, 0, 0, 1, 1, 1, 1),
ID = 1:8,
cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
doseGrid = seq(from = 25, to = 300, by = 25)
)
my_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = my_data
)
# Set-up some MCMC parameters and generate samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 800)
my_samples <- mcmc(my_data, my_model, my_options)
# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
# 'derive' is specified such that the 30% posterior quantile of the TD35 and
# TD30 samples will be used as TD35 and TD30 estimates.
tds_next_best <- NextBestTDsamples(
prob_target_drt = 0.35,
prob_target_eot = 0.3,
derive = function(samples) {
as.numeric(quantile(samples, probs = 0.3))
}
)
# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
nextBest = tds_next_best,
doselimit = max(my_data@doseGrid),
samples = my_samples,
model = my_model,
data = my_data
)
dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestMaxGain` NextBest class.
# Create the data.
my_data <- DataDual(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
ID = 1:8,
cohort = 1:8,
w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
doseGrid = seq(25, 300, 25),
placebo = FALSE
)
# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = my_data
)
# 'ModelEff' efficacy model, e.g. 'Effloglog'.
my_model_eff <- Effloglog(
eff = c(1.223, 2.513),
eff_dose = c(25, 300),
nu = c(a = 1, b = 0.025),
data = my_data
)
# Target probabilities of the occurrence of a DLT during trial and at the
# end of trial are defined as 0.35 and 0.3, respectively.
mg_next_best <- NextBestMaxGain(
prob_target_drt = 0.35,
prob_target_eot = 0.3
)
# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
nextBest = mg_next_best,
doselimit = 300,
model = my_model_dlt,
model_eff = my_model_eff,
data = my_data
)
dose_recommendation$next_dose
dose_recommendation$plot
# Example of usage for `NextBestMaxGainSamples` NextBest class.
# Create the data.
my_data <- DataDual(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
ID = 1:8,
cohort = 1:8,
doseGrid = seq(25, 300, 25),
placebo = FALSE
)
# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = my_data
)
# 'ModelEff' efficacy model, e.g 'Effloglog'.
my_model_effll <- Effloglog(
eff = c(1.223, 2.513),
eff_dose = c(25, 300),
nu = c(a = 1, b = 0.025),
data = my_data
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples_dlt <- mcmc(my_data, my_model_dlt, my_options)
my_samples_effll <- mcmc(my_data, my_model_effll, my_options)
# Target probabilities of the occurrence of a DLT during trial and at the end of
# trial are defined as 0.35 and 0.3, respectively.
# Use 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35
# and TD30.
# Use 50% posterior quantile of the Gstar (the dose which gives the maxim gain value)
# samples as Gstar estimate.
mgs_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))
}
)
dose_recommendation <- nextBest(
nextBest = mgs_next_best,
doselimit = max(my_data@doseGrid),
samples = my_samples_dlt,
model = my_model_dlt,
data = my_data,
model_eff = my_model_effll,
samples_eff = my_samples_effll
)
dose_recommendation$next_dose
dose_recommendation$plot
# Now using the 'EffFlexi' class efficacy model:
my_model_effflexi <- EffFlexi(
eff = c(1.223, 2.513),
eff_dose = c(25, 300),
sigma2W = c(a = 0.1, b = 0.1),
sigma2betaW = c(a = 20, b = 50),
rw1 = FALSE,
data = my_data
)
my_samples_effflexi <- mcmc(my_data, my_model_effflexi, my_options)
dose_recommendation <- nextBest(
nextBest = mgs_next_best,
doselimit = max(my_data@doseGrid),
samples = my_samples_dlt,
model = my_model_dlt,
data = my_data,
model_eff = my_model_effflexi,
samples_eff = my_samples_effflexi
)
dose_recommendation$next_dose
dose_recommendation$plot
# Example of usage for `NextBestProbMTDLTE` NextBest class.
# Create the data.
my_data <- Data(
x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
y = c(0, 0, 0, 0, 0, 0, 1, 0),
ID = 1:8,
cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
intervals = c(0, 20),
increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDLTE' class.
nb_mtd_lte <- NextBestProbMTDLTE(target = 0.33)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = nb_mtd_lte,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
# Example of usage for `NextBestProbMTDMinDist` NextBest class.
# Create the data.
my_data <- Data(
x = c(1.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5),
y = c(0, 0, 0, 0, 0, 0, 1, 1, 0),
ID = 1:9,
cohort = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
doseGrid = c(1.5, 2.5, 3.5, 4.5, 6, 7)
)
# Initialize the CRM model used to model the data.
my_model <- my_model <- LogisticKadaneBetaGamma(
theta = 0.3,
xmin = 1.5,
xmax = 7,
alpha = 1,
beta = 19,
shape = 0.5625,
rate = 0.125
)
# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 500)
my_samples <- mcmc(my_data, my_model, my_options)
# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsDoseLevels(levels = 1)
next_max_dose <- maxDose(my_increments, data = my_data)
# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDMinDist' class.
nb_mtd_min_dist <- NextBestProbMTDMinDist(target = 0.3)
# Calculate the next best dose.
dose_recommendation <- nextBest(
nextBest = nb_mtd_min_dist,
doselimit = next_max_dose,
samples = my_samples,
model = my_model,
data = my_data
)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)
nextBest(
nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
samples = ordinal_samples,
doselimit = Inf,
model = ordinal_model,
data = ordinal_data
)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)
nextBest(
nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
samples = ordinal_samples,
doselimit = Inf,
model = ordinal_model,
data = ordinal_data
)
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