design/devel/three.R

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

# nolint start

#####################################################################################
## Author: Daniel Sabanes Bove [sabanesd *a*t* roche *.* com]
## Project: Object-oriented implementation of CRM designs
##
## Time-stamp: <[three.R] by DSB Son 03/05/2015 20:30>
##
## Description:
## Test the 3+3 stuff. For development only!!
##
## History:
## 30/12/2014   file creation
## 03/05/2015   update with new syntax
###################################################################################

source("../R/helpers.R")
source("../R/Data-class.R")
source("../R/Data-methods.R")

## create some test data
data <- Data(
  x =
    c(
      0.1, 0.1, 0.1,
      10, 10, 10,
      10, 10, 10,
      20, 20, 20,
      30, 30, 30,
      60, 60, 60,
      60, 60, 60
    ),
  y =
    as.integer(c(
      1, 1, 0,
      0, 0, 1,
      0, 0, 0,
      0, 0, 0,
      0, 0, 0,
      0, 0, 0,
      1, 0, 0
    )),
  doseGrid =
    c(0.1, 10, 20, 30, 60)
)
data
data@nGrid

library(ggplot2)
plot(data)

## now on to the rules:
source("../R/Model-class.R")
source("../R/McmcOptions-class.R")
source("../R/McmcOptions-methods.R")
source("../R/mcmc.R")
source("../R/helpers.R")
source("../R/Samples-class.R")
source("../R/Rules-class.R")
source("../R/Rules-methods.R")

## next best method
myNextBest <- NextBestThreePlusThree()

nextDose <- nextBest(myNextBest, data = data)
nextDose$value
nextDose$stopHere
data


## test design
emptydata <- Data(doseGrid = c(0.1, 10, 20, 30, 60))

source("../R/Design-class.R")
design <- RuleDesign(
  nextBest = myNextBest,
  data = emptydata,
  cohort_size = CohortSizeConst(size = 3L),
  ## using a constant cohort size of 3,
  ## we obtain exactly the 3+3 design
  startingDose = 0.1
)

## alternative:
design <- ThreePlusThreeDesign(emptydata@doseGrid)



## ----------------------------------------
## todo: cont here


## for testing the simulate function:
object <- design
truth <- function(dose, alpha0, alpha1) {
  plogis(alpha0 + alpha1 * dose)
}

## args <- list(rho0=0.1,
##              gamma=20)
args <- list(
  alpha0 = -10,
  alpha1 = 0.3
)

curve(truth(x, args$alpha0, args$alpha1), from = 0.1, to = 60)
nsim <- 10L
seed <- 23

## iterSim <- 1L


source("../R/Simulations-class.R")
source("../R/Design-methods.R")

mySims <- simulate(design,
  truth = truth,
  args = args,
  nsim = 10L
)

str(mySims)

## look at simulated trial outcomes:
plot(mySims@data[[9]])
plot(mySims@data[[2]])


## final MTDs
mySims@doses

## what is the true prob at these doses?
truth(
  mySims@doses,
  args$alpha0, args$alpha1
)


## extract operating characteristics
source("../R/Simulations-methods.R")

## the truth we want to compare it with:
myTruth <- function(dose) {
  truth(dose, args$alpha0, args$alpha1)
}
curve(myTruth(x), from = 0, to = 60)

sumOut <- summary(mySims,
  truth = myTruth
)
sumOut

mySims@doses


## make nice plots for simulation output:
## first from the summary object
str(sumOut)

plot(sumOut)
plot(mySims)

plot(sumOut, type = c("nObs"))

## now from the raw simulation output
str(mySims@data)

plot(mySims)

## nice!

# nolint end