dose: Computing the Doses for a given independent variable, Model...
In Roche/crmPack: Object-Oriented Implementation of CRM Designs

dose	R Documentation

Computing the Doses for a given independent variable, Model and Samples

Description

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).

Usage

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)

Arguments

`x`	(`proportion` or `numeric`) a value of an independent variable on which dose depends. The following recycling rule applies when `samples` is not missing: vectors of size 1 will be recycled to the size of the sample (i.e. `size(samples)`). Otherwise, `x` must have the same size as the sample.
`model`	(`GeneralModel` or `ModelPseudo`) the model.
`samples`	(`Samples`) the samples of model's parameters that will be used to compute the resulting doses. Can also be missing for some models.
`...`	model specific parameters when `samples` are not used.
`grade`	(`integer`) The toxicity grade for which probabilities are required
`group`	(`character` or `factor`) for `LogisticLogNormalGrouped`, indicating whether to calculate the dose for the `mono` or for the `combo` arm.

Details

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 ⁠nSamples x dimParameter⁠.

Value

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.

Functions

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).

Note

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.

Examples

# 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)

Roche/crmPack documentation built on April 15, 2024, 8:06 a.m.