examples/Rules-method-nextBest-NextBestDualEndpoint.R

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

# 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)