prob: Computing Toxicity Probabilities for a Given Dose, Model and...

In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description

A function that computes the probability of the occurrence of a DLE at a specified dose level, based on the model parameters.

Compute toxicity probabilities of the occurrence of a DLE at a specified dose level, based on the samples of LogisticIndepBeta model parameters. The LogisticIndepBeta model is a Pseudo DLE (dose-limiting events)/toxicity.

Compute toxicity probabilities of the occurrence of a DLE at a specified dose level, based on the LogisticIndepBeta model parameters. The LogisticIndepBeta model is a Pseudo DLE (dose-limiting events)/toxicity. All model parameters (except dose) should be present in the model object.

Usage

prob(dose, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormal,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,LogisticLogNormal,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,ProbitLogNormal,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
prob(dose, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,missing'
prob(dose, model, samples, ...)

Arguments

dose	(number or numeric) the dose which is targeted. This must be a scalar if non-scalar samples are used. It can be a vector of any finite length, if samples are scalars or samples are not used, as e.g. in case of pseudo DLE (dose-limiting events)/toxicity model.
model	(GeneralModel or ModelTox) the model for single agent dose escalation or pseudo DLE (dose-limiting events)/toxicity model.
samples	(Samples) the samples of model's parameters that will be used to compute toxicity probabilities.
...	model specific parameters when samples are not used.

Details

The prob function computes the probability of toxicity for given doses, using samples of the model parameter(s). If you work with multivariate model parameters, then assume that your model specific prob 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 toxicity probabilities. 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 dose.

Note

The prob and dose functions are the inverse of each other.

See Also

probFunction, dose.

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 Prob(DLT | dose = 50).
prob(dose = 50, model = my_model, samples = my_samples)

# Create data from the 'DataDual' class.
data_dual <- 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),
  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 = data_dual
)

# Get samples from posterior.
dlt_sample <- mcmc(data = data_dual, model = dlt_model, options = my_options)

# Posterior for Prob(DLT | dose = 100).
prob(dose = 100, model = dlt_model, samples = dlt_sample)
prob(dose = c(50, 150), model = dlt_model)

0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.