design/devel/dualTestcode.R

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

library(crmPack)
PL <- 0.001
data <- Data(
  x = c(PL, 25, 25, 25, PL, 50, 50, 50, PL, 100, 100, 100),
  y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
  doseGrid = c(PL, seq(25, 300, 25)),
  ID = 1:12,
  placebo = TRUE
)
emptydata <- Data(doseGrid = data@doseGrid, placebo = TRUE)
DLTmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)
data2 <- DataDual(
  doseGrid = data@doseGrid,
  placebo = TRUE
)

## this gives an error message:
Effmodel <- Effloglog(
  Eff = c(1.223, 2.513),
  Effdose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data2
)

## but we can correct with setting c>=1:
Effmodel <- Effloglog(
  Eff = c(1.223, 2.513),
  Effdose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data2,
  c = 2
)


data3 <- DataDual(
  x = c(PL, 25, 25, 25, PL, 100, 100, 100, PL, 300, 300, 300),
  y = c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1),
  w = c(
    0, 0.2, 0.5, 1, 0, 1.9, 2.2, 2,
    0.03, 2.7, 2.6, 3
  ),
  cohort = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
  doseGrid = data@doseGrid,
  ID = 1:12,
  placebo = TRUE
)
newEffmodel <- update(object = Effmodel, data = data3)
newDLTmodel <- update(object = DLTmodel, data = data3)

GainNextBest <- NextBestMaxGain(
  DLEDuringTrialtarget = 0.35,
  DLEEndOfTrialtarget = 0.3
)
# plot of the efficacy curve
plot(data3, newEffmodel)

# probabilities of DLE at all dose
prob(data3@doseGrid, newDLTmodel)
DLE <- prob(data3@doseGrid, newDLTmodel)
ExpEff(data3@doseGrid, newEffmodel)

Eff <- ExpEff(data3@doseGrid, newEffmodel)

## calculate gain values at each dose levels including the placebo
gain(data3@doseGrid, DLEmodel = newDLTmodel, Effmodel = newEffmodel)

## plot continuously:
plotgrid <- seq(from = 0.001, to = 300, length = 300)
gainvals <- gain(plotgrid,
  DLEmodel = newDLTmodel,
  Effmodel = newEffmodel
)
effvals <- ExpEff(plotgrid, newEffmodel)
dlevals <- prob(plotgrid, newDLTmodel)

plot(plotgrid, effvals, type = "l", col = "blue")
lines(plotgrid, gainvals, col = "black")
lines(plotgrid, dlevals, col = "red")

## The follwoing is the same
Eff * (1 - DLE)


## Using the optim function to find the max gain value and Gstar
## Define inverse gain function
Gainfun <- function(DOSE) {
  -gain(DOSE, DLEmodel = newDLTmodel, Effmodel = newEffmodel)
}


LowestDose <- min(data3@doseGrid)

## Find the dose which gives the maximum gain
Gstar <- (optim(LowestDose, Gainfun, method = "L-BFGS-B", lower = LowestDose, upper = max(data@doseGrid))$par)
## Find the maximum gain value

MaxGain <- -(optim(LowestDose, Gainfun, method = "L-BFGS-B", lower = LowestDose, upper = max(data@doseGrid))$value)
## be sure which doses are ok with respect to maximum

## Value of Gstar, the dose with maximum gain value
Gstar
## The maximum gain value
MaxGain