Summarize the model-based design simulations, relative to a given truth

Usage

# S4 method for class 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

object: the Simulations object we want to summarize
truth: a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity
target: the target toxicity interval (default: 20-35%) used for the computations
...: Additional arguments can be supplied here for truth

Value

an object of class SimulationsSummary

Examples

# nolint start

# Define the dose-grid
emptydata <- 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
    ),
  ref_dose = 56
)

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

# Choose the rule for the cohort-size
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)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

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

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)
time <- system.time(mySims <- simulate(design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  mcmcOptions = options,
  parallel = FALSE,
  derive = list(
    max_mtd = max,
    mean_mtd = mean,
    median_mtd = median
  ),
))[3]

# Summarize the Results of the Simulations
summary(mySims, truth = myTruth)
#> Summary of 1 simulations
#> 
#> Target toxicity interval was 20, 35 %
#> Target dose interval corresponding to this was 19.6, 21.6 
#> Intervals are corresponding to 10 and 90 % quantiles
#> 
#> Number of patients overall : mean 19 (19, 19) 
#> Number of patients treated above target tox interval : mean 9 (9, 9) 
#> Proportions of DLTs in the trials : mean 32 % (32 %, 32 %) 
#> Mean toxicity risks for the patients on active : mean 35 % (35 %, 35 %) 
#> Doses selected as MTD : mean 20 (20, 20) 
#> True toxicity at doses selected : mean 22 % (22 %, 22 %) 
#> Proportion of trials selecting target MTD: 100 %
#> Dose most often selected as MTD: 20 
#> Observed toxicity rate at dose most often selected: 25 %
#> Fitted toxicity rate at dose most often selected : mean 27 % (27 %, 27 %) 
#> max_mtd : 72.73 
#> mean_mtd : 21.72 
#> median_mtd : 21.49 
#> Stop reason triggered:
#>  ≥ 3 cohorts dosed :  100 %
#>  P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5 :  100 %
#>  ≥ 20 patients dosed :  0 %

# nolint end