design/devel/tite-crm.R

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

# nolint start

### two examples of using crmPack to run a TITE-CRM design with overdose control

library("crmPack")
source("R/SafetyWindow.r")
source("R/TITE-CRM-class.r")
source("R/TITE-CRM-simulation.r")


## Example 1: recommend a dose for the next cohort;

# 1) Data

data <- DataTITE(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid =
    c(
      0.1, 0.5, 1.5, 3, 6,
      seq(from = 10, to = 80, by = 2)
    ),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, -15, -30, -40, -55, -70, -75, -85),
  Tmax = 60,
  weightMethod = "adaptive"
)

emptydata <- DataTITE(doseGrid = c(
  0.1, 0.5, 1, 1.5, 3, 6,
  seq(from = 2, to = 50, by = 2)
), Tmax = 42, weightMethod = "adaptive")


# 2) Structure of the model class

## need to fill in
Tmax_ <- 42

model <- TITELogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# 3) Obtain the posterior

options <- McmcOptions(
  burnin = 10,
  step = 2,
  samples = 1e2
)

set.seed(94)
samples <- mcmc(data, model, options)

# 4) use ggmcmc to diagnose

library(ggmcmc)
alpha0samples <- get(samples, "alpha0")

print(ggs_traceplot(alpha0samples))

print(ggs_autocorrelation(alpha0samples))


# 5) plot the model fit

plot(samples, model, data, hazard = TRUE)

plot(samples, model, data, hazard = FALSE)

# prior mean curve
emptydata <- DataTITE(doseGrid = c(
  0.1, 0.5, 1.5, 3, 6,
  seq(from = 10, to = 80, by = 2)
), Tmax = 60, weightMethod = "adaptive")

set.seed(94)
Priorsamples <- mcmc(emptydata, model, options)

plot(Priorsamples, model, emptydata, hazard = FALSE)


# 6) Escalation rules

## need to fill in (use the same rule in the section 8 of "using the package crmPack: introductory examples")
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

nextMaxDose <- maxDose(myIncrements, data = data)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  maxOverdoseProb = 0.25
)

mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2)


# 7) recommended dose for the next cohort

doseRecommendation <- nextBest(myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

doseRecommendation$value

doseRecommendation$plot

## Example 2: run a simulation to evaluate design operating characters;

# 1) set up safety window and DADesign
## to be completed
mysafetywindow <- SafetyWindowConst(c(6, 2), 10, 20)

design <- TITEDesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

# 2)set up truth curves

myTruth <- function(dose) {
  model@prob(dose, alpha0 = 2, alpha1 = 3)
}

curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))



onset <- 15

exp_cond.cdf <- function(x) {
  1 - (pexp(x, 1 / onset, lower.tail = FALSE) - pexp(28, 1 / onset, lower.tail = FALSE)) / pexp(28, 1 / onset)
}


# 3) set up simulation settings

mySims <- simulate(design,
  args = NULL,
  truthTox = myTruth,
  truthSurv = exp_cond.cdf, # piece_exp_cond.cdf,
  trueTmax = 80,
  nsim = 5,
  seed = 819,
  mcmcOptions = options,
  firstSeparate = TRUE,
  deescalate = FALSE,
  parallel = FALSE
)

# system.time(simulate(design,
#                      args=NULL,
#                      truthTox=myTruth,
#                      truthSurv=exp_cond.cdf,#piece_exp_cond.cdf,
#                      trueTmax=80,
#                      nsim=500,
#                      seed=819,
#                      mcmcOptions=options,
#                      firstSeparate=TRUE,
#                      deescalate=FALSE,
#                      parallel=FALSE))


# 4) interprate simulation result
# use a similar way as section 9.2 in the "using the package crmPack: introductory examples" document
a <- summary(mySims, truth = myTruth)

plot(mySims)

mySims@stopReasons[[3]]

savePlot <- function(myPlot, name) {
  png(filename = paste(Sys.Date(), "C:/Users/liaoz4/Documents/R/simulation_result/", name, ".png", sep = ""), width = 480, height = 480)
  print(myPlot)
  dev.off()
}

# nolint end