dose | R Documentation |
A function that computes the dose reaching a specific target value of a given variable that dose depends on. The meaning of this variable depends on the type of the model. For instance, for single agent dose escalation model or pseudo DLE (dose-limiting events)/toxicity model, this variable represents the a probability of the occurrence of a DLE. For efficacy models, it represents expected efficacy. The doses are computed based on the samples of the model parameters (samples).
dose(x, model, samples, ...)
## S4 method for signature 'numeric,LogisticNormal,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticLogNormal,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticLogNormalOrdinal,Samples'
dose(x, model, samples, grade)
## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,ProbitLogNormal,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticLogNormalGrouped,Samples'
dose(x, model, samples, group)
## S4 method for signature 'numeric,LogisticKadane,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticKadaneBetaGamma,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticNormalMixture,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticNormalFixedMixture,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,DualEndpoint,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,LogisticIndepBeta,missing'
dose(x, model)
## S4 method for signature 'numeric,Effloglog,missing'
dose(x, model)
## S4 method for signature 'numeric,EffFlexi,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,OneParLogNormalPrior,Samples'
dose(x, model, samples)
## S4 method for signature 'numeric,OneParExpPrior,Samples'
dose(x, model, samples)
x |
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model |
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samples |
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... |
model specific parameters when |
grade |
( |
group |
( |
The dose()
function computes the doses corresponding to a value of
a given independent variable, using samples of the model parameter(s).
If you work with multivariate model parameters, then assume that your model
specific dose()
method receives a samples matrix where the rows
correspond to the sampling index, i.e. the layout is then
ā nSamples x dimParameterā
.
A number
or numeric
vector with the doses.
If non-scalar samples
were used, then every element in the returned vector
corresponds to one element of a sample. Hence, in this case, the output
vector is of the same length as the sample vector. If scalar samples
were
used or no samples
were used, e.g. for pseudo DLE/toxicity model
,
then the output is of the same length as the length of the prob
.
dose(x = numeric, model = LogisticNormal, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticLogNormal, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticLogNormalOrdinal, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
In the case of a LogisticLogNormalOrdinal
model, dose
returns only the
probability of toxicity at the given grade or higher
dose(x = numeric, model = LogisticLogNormalSub, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = ProbitLogNormal, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = ProbitLogNormalRel, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticLogNormalGrouped, samples = Samples)
: method for LogisticLogNormalGrouped
which needs group
argument in addition.
dose(x = numeric, model = LogisticKadane, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticKadaneBetaGamma, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticNormalMixture, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticNormalFixedMixture, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticLogNormalMixture, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = DualEndpoint, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticIndepBeta, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
dose(x = numeric, model = LogisticIndepBeta, samples = missing)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
All model parameters (except x
) should be present in the model
object.
dose(x = numeric, model = Effloglog, samples = missing)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
).
All model parameters (except x
) should be present in the model
object.
dose(x = numeric, model = EffFlexi, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLE (x
). For this method x
must
be a scalar.
dose(x = numeric, model = OneParLogNormalPrior, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLT (x
).
dose(x = numeric, model = OneParExpPrior, samples = Samples)
: compute the dose level reaching a specific target
probability of the occurrence of a DLT (x
).
The dose()
and prob()
methods are the inverse of each other, for
all dose()
methods for which its first argument, i.e. a given independent
variable that dose depends on, represents toxicity probability.
doseFunction()
, prob()
, efficacy()
.
# Create some 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),
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 a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
ref_dose = 56
)
# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = my_model, samples = my_samples)
# Create data from the 'Data' (or 'DataDual') class.
dlt_data <- Data(
x = c(25, 50, 25, 50, 75, 300, 250, 150),
y = c(0, 0, 0, 0, 0, 1, 1, 0),
doseGrid = seq(from = 25, to = 300, by = 25)
)
# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
binDLE = c(1.05, 1.8),
DLEweights = c(3, 3),
DLEdose = c(25, 300),
data = dlt_data
)
# Get samples from posterior.
dlt_sample <- mcmc(data = dlt_data, model = dlt_model, options = my_options)
# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = dlt_model, samples = dlt_sample)
dose(x = c(0.45, 0.6), model = dlt_model)
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data_ordinal, model, options)
dose(0.25, model, samples, grade = 2L)
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