gain: Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy...

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples.

Description

Usage

gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,Samples,ModelEff,Samples'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

Arguments

dose	(number or numeric) the dose which is targeted. The following recycling rule applies when samples are not missing: vectors of size 1 will be recycled to the size of the sample. Otherwise, dose must have the same size as the sample.
model_dle	(ModelTox) pseudo DLE (dose-limiting events)/toxicity model.
samples_dle	(Samples) the samples of model's parameters that will be used to compute toxicity probabilities. Can also be missing for some models.
model_eff	(ModelEff) the efficacy model with pseudo data prior.
samples_eff	(Samples) samples of model's parameters that will be used to compute expected efficacy values. Can also be missing for some models.
...	not used.

Details

This function computes the gain values for a given dose level, pseudo DLE and Efficacy models as well as a given DLE and Efficacy samples.

Value

The gain values.

Functions

• gain( dose = numeric, model_dle = ModelTox, samples_dle = Samples, model_eff = ModelEff, samples_eff = Samples ):

• gain( dose = numeric, model_dle = ModelTox, samples_dle = missing, model_eff = Effloglog, samples_eff = missing ): Compute the gain value for a given dose level, pseudo DLE and Efficacy models without DLE and the Efficacy samples.

Examples

# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# as well as DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

samples_dle <- mcmc(emptydata, model_dle, mcmc_opts)

# Efficacy model (Effloglog) and samples.
model_effloglog <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

samples_effloglog <- mcmc(emptydata, model_effloglog, mcmc_opts)

# Gain values for dose level 75 and Effloglog efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effloglog,
  samples_eff = samples_effloglog
)

# Efficacy model (EffFlexi) and samples.
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 = emptydata
)

samples_effflexi <- mcmc(emptydata, model_effflexi, mcmc_opts)

# Gain values for dose level 75 and EffFlexi efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effflexi,
  samples_eff = samples_effflexi
)

# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# without DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
data <- Data(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

# Efficacy model and samples.
model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# Gain value for dose level 75.
gain(
  dose = 75,
  model_dle = model_dle,
  model_eff = model_eff
)

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.