README.md

crmPack

The goal of crmPack is to implement a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with your own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules.

Installation

You can install the development version of crmPack from github with:

devtools::install_github("Roche/crmPack")

You can install the stable release version of crmPack from CRAN with:

install.packages("crmPack")

Example

This is a basic example which shows how to run simulations from a CRM with a 2-parameter logistic regression model, using a log normal prior distribution, and custom cohort size, stopping and maximum increments rules:

library(crmPack)

# Define the dose grid.
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  refDose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  maxOverdoseProb = 0.25
)

# Choose the rule for the cohort size.
my_size_1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohortSize = c(1, 3)
)
my_size_2 <- CohortSizeDLT(
  DLTintervals = c(0, 1),
  cohortSize = c(1, 3)
)
my_size <- maxSize(my_size_1, my_size_2)

# Choose the rule for stopping.
my_stopping_1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping_2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping_3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping_1 & my_stopping_2) | my_stopping_3

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- Design(
  model = model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohortSize = my_size,
  data = empty_data,
  startingDose = 3
)

# Define the true function.
my_truth <- function(dose) {
  alpha0 <- 7
  alpha1 <- 8
  ref_dose <- 56
  stand_log_dose <- log(dose / ref_dose)
  plogis(alpha0 + alpha1 * stand_log_dose)
}

# Run the simulation on the desired design.
# We only generate 1 trial outcome here for illustration, for the actual study
# this should be increased of course.
options <- McmcOptions(
  burnin = 100,
  step = 1,
  samples = 2000
)
time <- system.time(my_sims <- simulate(design,
  args = NULL,
  truth = my_truth,
  nsim = 1,
  seed = 819,
  mcmcOptions = options,
  parallel = FALSE
))[3]