devel/combo.R

In 0liver0815/onc-crmpack-test: 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: <[combo.R] by DSB Sam 07/03/2015 21:58>
##
## Description:
## Test the combo stuff. For development only!!
##
## History:
## 25/01/2015   file creation
###################################################################################

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


## create some test data
data <- DataCombo(x=
                      cbind(a=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
                            b=c(20, 20, 20, 40, 40, 40, 50, 50)),
                  y=c(0, 0, 0, 1, 0, 0, 1, 1),
                  doseGrid=
                      list(a=
                               c(0.1, 0.5, 1.5, 3, 6,
                                 seq(from=10, to=80, by=2)),
                           b=seq(from=10, to=80, by=10)))

data
data@nGrid
data@nObs

## now test updating and plotting the data
source("../R/Data-methods.R")

## updating:
data2 <- update(data,
                x=c(a=0.5, b=30),
                y=c(0, 1, 0, 0))

## plotting:
library(ggplot2)
x11()
plot(data)

## load model code
source("../R/Model-class.R")

## define the model
model <- ComboLogistic(singlePriors=
                           list(a=
                                    LogisticLogNormal(mean=c(0, 1),
                                                      cov=diag(2),
                                                      refDose=10),
                                b=
                                    LogisticLogNormal(mean=c(1, 2),
                                                      cov=diag(2),
                                                      refDose=20)),
                       gamma=0,
                       tau=0.4)


## try sampling from the model:

source("../R/McmcOptions-class.R")
source("../R/McmcOptions-methods.R")
## and some MCMC options
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=50000)


source("../R/mcmc.R")
source("../R/helpers.R")
source("../R/Samples-class.R")

## obtain the samples
library(rjags)

samples <- mcmc(data, model, options, verbose=TRUE)

str(samples)

source("../R/Samples-methods.R")

## use the ggmcmc package for convergence checks. we provide the extract function
## for this purpose:
library(ggmcmc)

alpha0samples <- get(samples, "alpha0")
ggs_traceplot(alpha0samples)

alpha1samples <- get(samples, "alpha1")
ggs_traceplot(alpha1samples)

ggs_traceplot(get(samples, "eta"))

## ok now we want to plot the fit:
source("../R/Model-methods.R")

## test C++ inline. requires MinGW installation and path settings,
## see http://stackoverflow.com/questions/23458841/how-to-get-rcpp-to-work
library(Rcpp)

cppFunction('
int fibonacci(const int x) {
if (x < 2)
return x;
else
return (fibonacci(x - 1)) + fibonacci(x - 2);
}
')
fibonacci(5)


## todo: cont here
system.time(print(plot(samples, model, data, focus=c("a", "b"))))

## old:
##   user  system elapsed
##  73.40    0.25   73.74

## new:

## user  system elapsed
## 5.27    0.20    5.51

## after first run even slightly faster:
   ## user  system elapsed
## 4.87    0.15    5.02

## ==> ~15 times faster with C++!
## nice!

x11()
plot(samples, model, data, extrapolate=FALSE)

betaModList <- list(betaMod = rbind(c(1,1), c(1.5,0.75), c(0.8,2.5), c(0.4,0.9)))
plotModels(betaModList, c(0,1), base = 0, maxEff = 1, scal = 1.2)


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

## target level is 90% of maximum biomarker level
## overdose tox interval is 35%+
myNextBest <- new("NextBestDualEndpoint",
                  target=0.9,
                  overdose=c(0.35, 1),
                  maxOverdoseProb=0.25)

nextDose <- nextBest(myNextBest, doselimit=50, samples=samples, model=model, data=data)
nextDose$plot
nextDose$value
data


## stopping rule:
## min 3 cohorts and at least 50% prob in for targeting biomarker,
## or max 20 patients
myStopping1 <- new("StoppingMinCohorts",
                   nCohorts=3L)
myStopping2 <- new("StoppingMaxPatients",
                   nPatients=50L)
myStopping3 <- new("StoppingTargetBiomarker",
                   target=0.9,
                   prob=0.5)

## you can either write this:
myStopping <- new("StoppingAny",
                  stopList=
                  list(new("StoppingAll",
                           stopList=
                           list(myStopping1,
                                myStopping3)),
                       myStopping2))

## or much more intuitively:
myStoppingEasy <- (myStopping1 & myStopping3) | myStopping2
myStoppingEasy
identical(myStopping, myStoppingEasy)


stopTrial(myStopping, dose=nextDose$value,
          samples, model, data)

## relative increments:
myIncrements <- new("IncrementsRelative",
                    intervals=c(0, 20, Inf),
                    increments=c(1, 0.33))

## test design
source("../R/Design-class.R")
design <- new("DualDesign",
              model=model,
              nextBest=myNextBest,
              stopping=myStopping,
              increments=myIncrements,
              data=
              new("DataDual",
                  x=numeric(),
                  y=integer(),
                  w=numeric(),
                  doseGrid=
                  c(0.1, 0.5, 1.5, 3, 6,
                    seq(from=10, to=80, by=2))),
              cohortSize=new("CohortSizeConst", size=3L),
              startingDose=6)



## for testing the simulate function:
## object <- design
## truth <- model@prob
## ## args <- list(rho0=0.1,
## ##              gamma=20)
## args <- list(alpha0=0,
##              alpha1=1)
## nsim <- 10L
mcmcOptions <- new("McmcOptions")
seed <- 23

## iterSim <- 1L


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

betaMod <-
    function (dose, e0, eMax, delta1, delta2, scal)
{
    maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 +
                                                                     delta2))
    dose <- dose/scal
    e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}
##trace(simulate, browser, signature=c("DualDesign"))
trueTox <- function(dose){
    pnorm((dose-60)/10)
}
trueBiomarker <- function(dose){
    betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.001,
                   rho=0,
                   nsim=3L,
                   firstSeparate=FALSE,
                   parallel=FALSE,
                   seed=3)

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

str(mySims, 2)
str(mySims@fitBiomarker, 2)
identical(mySims@fitBiomarker[[1]],
          mySims@fitBiomarker[[2]])
identical(mySims@fitBiomarker[[2]],
          mySims@fitBiomarker[[3]])

plot(mySims@fitBiomarker[[1]]$middleBiomarker, type="l")

mySims@stopReasons
plot(mySims, type=c("dose", "rho"))
plot(mySims)


## look at simulated trial outcomes:
plot(mySims@data[[2]])
mySims@stopReasons[[3]]

## final MTDs
mySims@doses



## extract operating characteristics


## the truth we want to compare it with:
sumOut <- summary(mySims,
                  trueTox=trueTox,
                  trueBiomarker=trueBiomarker)
sumOut

mySims@doses


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

plot(sumOut)

plot(sumOut, type="meanFit")

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

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

plot(mySims)

# nolint end