# Rolling CRM Example

#### Jiawen Zhu, Daniel Sabanés Bové

#### 02 January 2019

## Example 1: Recommend a dose for the next cohort

### Setting up the data

    library(crmPack)
    data <- DataDA(
      x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
      y = c(0, 0, 1, 1, 0, 0, 1, 0),
      ID = as.integer(1:8),
      cohort = as.integer(c(1, 2, 3, 4, 5, 6, 6, 6)),
      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 = rep(0, 8),
      Tmax = 60
    )

    emptydata <- DataDA(
      doseGrid = c(
        0.1, 0.5, 1, 1.5, 3, 6,
        seq(from = 10, to = 80, by = 2)
      ),
      Tmax = 60
    )

### Structure of the model class

    npiece_ <- 10
    Tmax_ <- 60

    lambda_prior <- function(k) {
      npiece_ / (Tmax_ * (npiece_ - k + 0.5))
    }

    model <- DALogisticLogNormal(
      mean = c(-0.85, 1),
      cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
      ref_dose = 56,
      npiece = npiece_,
      l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
      c_par = 2
    )

### Obtain the posterior

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

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

### Use ggmcmc to diagnose

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

    print(ggs_traceplot(alpha0samples))

plot of chunk Diagnose-1

    print(ggs_autocorrelation(alpha0samples))

plot of chunk Diagnose-2

### Plot the model fit

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

plot of chunk Fit-1

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

plot of chunk Fit-2

### prior mean curve

    emptydata <- DataDA(doseGrid = c(
      0.1, 0.5, 1.5, 3, 6,
      seq(from = 10, to = 80, by = 2)
    ), Tmax = 60)

    Priorsamples <- mcmc(emptydata, model, options)

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

plot of chunk Prior

### 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),
      max_overdose_prob = 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)

### Recommended dose for the next cohort

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

    doseRecommendation$plot

plot of chunk Recommend

    doseRecommendation$value
    #> [1] 0.1

## Example 2: Run a simulation to evaluate operating characteristics

### Set up safety window and `DADesign` to be completed

    mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

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

### Set up true curves

    myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
    curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))

plot of chunk Truth

    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)
    }

### Perform the simulations

    mySims <- simulate(design,
      args = NULL,
      truthTox = myTruth,
      truthSurv = exp_cond.cdf,
      trueTmax = 80,
      nsim = 2,
      seed = 819,
      mcmcOptions = options,
      firstSeparate = TRUE,
      deescalate = FALSE,
      parallel = FALSE
    )

### Interpret the simulation results

Use a similar way as section 9.2 in the “using the package crmPack: introductory examples” document

    a <- summary(mySims, truth = myTruth)
    b <- mySims@data[[1]]

    plot(mySims)
    plot(b)

plot of chunk Interpret

    mySims@stop_reasons[[2]]
    #> [[1]]
    #> [1] "Probability for target toxicity is 68 % for dose 34 and thus above the required 50 %"
    #>
    #> [[2]]
    #> [1] "Number of patients is 17 and thus below the prespecified minimum number 50"

    # nolint end