R/Simulations-methods.R
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs
#####################################################################################
## Author: Daniel Sabanes Bove [sabanesd *a*t* roche *.* com],
## Wai Yin Yeung [ w *.* yeung1 *a*t* lancaster *.* ac *.* uk]
## Project: Object-oriented implementation of CRM designs
##
## Time-stamp: <[Simulations-methods.R] by DSB Fre 16/01/2015 13:41>
##
## Description:
## Methods for handling the simulations output.
##
## History:
## 19/02/2014 file creation
## 30/07/2014 added in methods for pseudo models simulations
###################################################################################
##' @include Simulations-class.R
##' @include helpers.R
{}
##' Plot simulations
##'
##' Summarize the simulations with plots
##'
##' This plot method can be applied to \code{\linkS4class{GeneralSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} } You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{GeneralSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMsingle.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="GeneralSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## summary of the trajectories
if("trajectory" %in% type)
{
## get a matrix of the simulated dose trajectories,
## where the rows correspond to the simulations and
## the columns to the patient index:
## If design with placebo, then exclude placebo Patients
if(x@data[[1]]@placebo){
PL <- x@data[[1]]@doseGrid[1]
simDoses <- lapply(x@data,
function(y){ y@x[y@x != PL]})
}else{
simDoses <- lapply(x@data,
slot,
"x")
}
maxPatients <- max(sapply(simDoses, length))
simDosesMat <- matrix(data=NA,
nrow=length(simDoses),
ncol=maxPatients)
for(i in seq_along(simDoses))
{
simDosesMat[i, seq_along(simDoses[[i]])] <-
simDoses[[i]]
}
## extract statistics
stats <- c("Minimum",
"Lower Quartile",
"Median",
"Upper Quartile",
"Maximum")
traj.df <-
data.frame(patient=
rep(seq_len(maxPatients), each=5L),
Statistic=
factor(rep(stats,
maxPatients),
levels=stats),
traj=
c(apply(simDosesMat, 2L, quantile,
na.rm=TRUE)))
## linetypes for the plot
lt <- c("Median" = 1,
"Lower Quartile" = 2,
"Upper Quartile" = 2,
"Minimum" = 4,
"Maximum"= 4)
## save the plot
if(x@data[[1]]@placebo){
myTitle <- "Patient (placebo were excluded)"
}else{
myTitle <- "Patient"
}
plotList[[plotIndex <- plotIndex + 1L]] <-
ggplot() +
geom_step(aes(x=patient,
y=traj,
group=Statistic,
linetype=Statistic),
size=1.2, colour="blue", data=traj.df) +
## scale_linetype_manual(values=lt) +
xlab(myTitle) + ylab("Dose Level")
}
## average distribution of the doses tried
if("dosesTried" %in% type)
{
## get the doses tried
simDoses <- lapply(x@data,
slot,
"x")
## get the dose distributions by trial
doseDistributions <-
sapply(simDoses,
function(s){
prop.table(table(factor(s,
levels=
x@data[[1]]@doseGrid)))
})
## derive the average dose distribution across trial
## simulations
averageDoseDist <- rowMeans(doseDistributions)
## get in data frame shape
dat <- data.frame(dose=as.numeric(names(averageDoseDist)),
perc=averageDoseDist * 100)
## produce and save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
ggplot() +
geom_bar(data=as.data.frame(dat),
aes(x=dose, y=perc),
stat="identity",
position="identity",
width=min(diff(x@data[[1]]@doseGrid)) / 2) +
xlab("Dose level") +
ylab("Average proportion [%]")
}
## then finally plot everything
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just return it
return(plotList[[1L]])
} else {
## otherwise arrange them
ret <- do.call(gridExtra::arrangeGrob,
plotList)
return(ret)
}
})
##' Plot dual-endpoint simulations
##'
##' This plot method can be applied to \code{\linkS4class{DualSimulations}}
##' objects in order to summarize them graphically. In addition to the standard
##' plot types, there is
##' \describe{
##' \item{sigma2W}{Plot a boxplot of the final biomarker variance estimates in
##' the simulated trials}
##' \item{rho}{Plot a boxplot of the final correlation estimates in
##' the simulated trials}
##' }
##'
##' @param x the \code{\linkS4class{DualSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 qplot coord_flip scale_x_discrete
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-DualSimulations.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="DualSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2W",
"rho"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,
c("sigma2W", "rho"))
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## biomarker variance estimates boxplot
if("sigma2W" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2West), geom="boxplot",
xlab="", ylab="Biomarker variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## correlation estimates boxplot
if("rho" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@rhoEst), geom="boxplot",
xlab="", ylab="Correlation estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret)
}
return(ret)
})
##' Summarize the simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{GeneralSimulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{GeneralSimulationsSummary}}
##'
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="GeneralSimulations"),
def=
function(object,
truth,
target=c(0.2, 0.35),
...){
## extract dose grid
doseGrid <- object@data[[1]]@doseGrid
## evaluate true toxicity at doseGrid
trueTox <- truth(doseGrid, ...)
## find dose interval corresponding to target tox interval
targetDoseInterval <-
sapply(target,
function(t){
## we have to be careful because it might be
## that in the range of the dose grid, no
## doses can be found that match the target
## interval boundaries!
## In that case we want to return NA
r <- try(uniroot(f=function(x){truth(x, ...) - t},
interval=
range(doseGrid))$root,
silent=TRUE)
if(inherits(r, "try-error"))
{
return(NA_real_)
} else {
return(r)
}
})
## what are the levels above target interval?
xAboveTarget <- which(trueTox > target[2])
## proportion of DLTs in a trial:
if(object@data[[1]]@placebo){
if( sum(object@data[[1]]@x == doseGrid[1]) ){
propDLTs <- sapply(object@data,
function(d){
tapply(d@y,
factor(d@x == d@doseGrid[1],
labels=c("ACTV","PLCB")),
mean)
})
}else{
propDLTs <- sapply(object@data,
function(d){
c('ACTV' = mean(d@y),'PLCB' = NA)
})
}
}else{
propDLTs <- sapply(object@data,
function(d){
mean(d@y)
})
}
## mean toxicity risk
if(object@data[[1]]@placebo){
meanToxRisk <- sapply(object@data,
function(d){
mean(trueTox[d@xLevel[d@xLevel != 1]])
})
}else{
meanToxRisk <- sapply(object@data,
function(d){
mean(trueTox[d@xLevel])
})
}
## doses selected for MTD
doseSelected <- object@doses
## replace NA by 0
doseSelected[is.na(doseSelected)] <- 0
## dose most often selected as MTD
doseMostSelected <-
as.numeric(names(which.max(table(doseSelected))))
xMostSelected <-
matchTolerance(doseMostSelected,
table=doseGrid)
## observed toxicity rate at dose most often selected
## Note: this does not seem very useful!
## Reason: In case of a fine grid, few patients if any
## will have been treated at this dose.
tmp <-
sapply(object@data,
function(d){
whichAtThisDose <- which(d@x == doseMostSelected)
nAtThisDose <- length(whichAtThisDose)
nDLTatThisDose <- sum(d@y[whichAtThisDose])
return(c(nAtThisDose=nAtThisDose,
nDLTatThisDose=nDLTatThisDose))
})
obsToxRateAtDoseMostSelected <-
mean(tmp["nDLTatThisDose",]) / mean(tmp["nAtThisDose",])
## number of patients overall
if(object@data[[1]]@placebo){
nObs <- sapply(object@data,
function(x){
data.frame(n.ACTV = sum(x@xLevel != 1L),
n.PLCB = sum(x@xLevel == 1L))
})
nObs <- matrix(unlist(nObs), dim(nObs))
}else{
nObs <- sapply(object@data,
slot,
"nObs")
}
## number of patients treated above target tox interval
nAboveTarget <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTarget)
})
## Proportion of trials selecting target MTD
toxAtDoses <- truth(doseSelected, ...)
propAtTarget <- mean((toxAtDoses > target[1]) &
(toxAtDoses < target[2]))
## give back an object of class GeneralSimulationsSummary,
## for which we then define a print / plot method
ret <-
.GeneralSimulationsSummary(
target=target,
targetDoseInterval=targetDoseInterval,
nsim=length(object@data),
propDLTs=propDLTs,
meanToxRisk=meanToxRisk,
doseSelected=doseSelected,
doseMostSelected=doseMostSelected,
obsToxRateAtDoseMostSelected=obsToxRateAtDoseMostSelected,
nObs=nObs,
nAboveTarget=nAboveTarget,
toxAtDosesSelected=toxAtDoses,
propAtTarget=propAtTarget,
doseGrid=doseGrid,
placebo=object@data[[1]]@placebo)
return(ret)
})
##' Summarize the model-based design simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{Simulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{SimulationsSummary}}
##'
##' @example examples/Simulations-method-summary.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="Simulations"),
def=
function(object,
truth,
target=c(0.2, 0.35),
...){
## call the parent method
start <- callNextMethod(object=object,
truth=truth,
target=target,
...)
doseGrid <- object@data[[1]]@doseGrid
## dose level most often selected as MTD
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
## fitted toxicity rate at dose most often selected
fitAtDoseMostSelected <-
sapply(object@fit,
function(f){
f$middle[xMostSelected]
})
## mean fitted toxicity (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanFitMatrix <- sapply(object@fit,
"[[",
"middle")
meanFit <- list(truth=
truth(doseGrid, ...),
average=
rowMeans(meanFitMatrix),
lower=
apply(meanFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanFitMatrix,
1L,
quantile,
0.975))
## give back an object of class SimulationsSummary,
## for which we then define a print / plot method
ret <- .SimulationsSummary(
start,
fitAtDoseMostSelected=fitAtDoseMostSelected,
meanFit=meanFit)
return(ret)
})
##' Summarize the dual-endpoint design simulations, relative to given true
##' dose-toxicity and dose-biomarker curves
##'
##' @param object the \code{\linkS4class{DualSimulations}} object we want to
##' summarize
##' @param trueTox a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity.
##' @param trueBiomarker a function which takes as input a dose (vector) and
##' returns the true biomarker level (vector).
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{trueTox}
##' and \code{trueBiomarker}
##' @return an object of class \code{\linkS4class{DualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summary-DualSimulations.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="DualSimulations"),
def=
function(object,
trueTox,
trueBiomarker,
target=c(0.2, 0.35),
...){
## call the parent method
start <- callNextMethod(object=object,
truth=trueTox,
target=target,
...)
doseGrid <- object@data[[1]]@doseGrid
## dose level most often selected as MTD
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
## fitted biomarker level at dose most often selected
biomarkerFitAtDoseMostSelected <-
sapply(object@fitBiomarker,
function(f){
f$middleBiomarker[xMostSelected]
})
## mean fitted biomarker curve (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanBiomarkerFitMatrix <- sapply(object@fitBiomarker,
"[[",
"middleBiomarker")
meanBiomarkerFit <- list(truth=
trueBiomarker(doseGrid, ...),
average=
rowMeans(meanBiomarkerFitMatrix),
lower=
apply(meanBiomarkerFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanBiomarkerFitMatrix,
1L,
quantile,
0.975))
## give back an object of class DualSimulationsSummary,
## for which we then define a print / plot method
ret <- .DualSimulationsSummary(
start,
biomarkerFitAtDoseMostSelected=biomarkerFitAtDoseMostSelected,
meanBiomarkerFit=meanBiomarkerFit)
return(ret)
})
##' A Reference Class to represent sequentially updated reporting objects.
##' @name Report
##' @field object The object from which to report
##' @field df the data frame to which columns are sequentially added
##' @field dfNames the names to which strings are sequentially added
Report <-
setRefClass("Report",
fields =
list(object = "ANY",
df = "data.frame",
dfNames = "character"),
methods = list(
dfSave =
function(res, name) {
df <<- cbind(df, res)
dfNames <<- c(dfNames, name)
return(res)
},
report =
function(slotName,
description,
percent=TRUE,
digits=0,
quantiles=c(0.1, 0.9),
subset=NULL,
doSum=FALSE) {
vals <- slot(object, name=slotName)
if(! is.null(subset))
vals <- vals[subset,]
if(doSum)
vals <- apply(vals, 2, sum)
if(percent)
{
unit <- " %"
vals <- vals * 100
} else {
unit <- ""
}
res <- paste(round(mean(vals), digits),
unit,
" (",
paste(round(quantile(vals,
quantiles,
na.rm=TRUE),
digits),
unit,
collapse=", ",
sep=""),
")",
sep="")
## print result to the buffer
cat(description, ":",
"mean",
dfSave(res, slotName),
"\n")
}))
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{GeneralSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="GeneralSimulationsSummary"),
def=
function(object){
r <- Report$new(object=object,
df=
as.data.frame(matrix(nrow=1,
ncol=0)),
dfNames=character())
cat("Summary of",
r$dfSave(object@nsim, "nsim"),
"simulations\n\n")
cat("Target toxicity interval was",
r$dfSave(paste(round(object@target * 100),
collapse=", "),
"targetToxInterval"),
"%\n")
cat("Target dose interval corresponding to this was",
r$dfSave(paste(round(object@targetDoseInterval, 1),
collapse=", "),
"targetDoseInterval"),
"\n")
cat("Intervals are corresponding to",
"10 and 90 % quantiles\n\n")
if(object@placebo){
r$report("nObs",
"Number of patients on placebo",
percent=FALSE,
subset=2)
r$report("nObs",
"Number of patients on active",
percent=FALSE,
subset=1)
r$report("nObs",
"Number of patients overall",
percent=FALSE,
doSum=TRUE)
}else{
r$report("nObs",
"Number of patients overall",
percent=FALSE)
}
r$report("nAboveTarget",
"Number of patients treated above target tox interval",
percent=FALSE)
if(object@placebo){
r$report("propDLTs",
"Proportions of DLTs in the trials for patients on placebo",
subset=2)
r$report("propDLTs",
"Proportions of DLTs in the trials for patients on active",
subset=1)
}else{
r$report("propDLTs",
"Proportions of DLTs in the trials")
}
r$report("meanToxRisk",
"Mean toxicity risks for the patients on active")
r$report("doseSelected",
"Doses selected as MTD",
percent=FALSE, digits=1)
r$report("toxAtDosesSelected",
"True toxicity at doses selected")
cat("Proportion of trials selecting target MTD:",
r$dfSave(object@propAtTarget * 100,
"percentAtTarget"),
"%\n")
cat("Dose most often selected as MTD:",
r$dfSave(object@doseMostSelected,
"doseMostSelected"),
"\n")
cat("Observed toxicity rate at dose most often selected:",
r$dfSave(round(object@obsToxRateAtDoseMostSelected * 100),
"obsToxRateAtDoseMostSelected"),
"%\n")
## finally assign names to the df
## and return it invisibly
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{SimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-show-SimulationsSummary.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="SimulationsSummary"),
def=
function(object){
## call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
## start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
## add one reporting line
r$report("fitAtDoseMostSelected",
"Fitted toxicity rate at dose most often selected")
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Show the summary of the dual-endpoint simulations
##'
##' @param object the \code{\linkS4class{DualSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-show-DualSimulationsSummary.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="DualSimulationsSummary"),
def=
function(object){
## call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
## start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
## add one reporting line
r$report("biomarkerFitAtDoseMostSelected",
"Fitted biomarker level at dose most often selected",
percent=FALSE,
digits=1)
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Graphical display of the general simulation summary
##'
##' This plot method can be applied to
##' \code{\linkS4class{GeneralSimulationsSummary}} objects in order to
##' summarize them graphically. Possible \code{type}s of plots at the moment
##' are:
##'
##' \describe{
##' \item{nObs}{Distribution of the number of patients in the simulated trials}
##' \item{doseSelected}{Distribution of the final selected doses in the trials.
##' Note that this can include zero entries, meaning that the trial was stopped
##' because all doses in the dose grid appeared too toxic.}
##' \item{propDLTs}{Distribution of the proportion of patients with DLTs in the
##' trials}
##' \item{nAboveTarget}{Distribution of the number of patients treated at doses
##' which are above the target toxicity interval (as specified by the
##' \code{truth} and \code{target} arguments to
##' \code{\link{summary,GeneralSimulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{GeneralSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="GeneralSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## distribution of overall sample size
if(x@placebo){
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs[2,],
description="Number of patients on active in total")
}
}else{
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs,
description="Number of patients in total")
}
}
## distribution of final MTD estimate
if("doseSelected" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@doseSelected,
description="MTD estimate")
}
## distribution of proportion of DLTs
if(x@placebo){
if("propDLTs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLTs[1,] * 100,
description="Proportion of DLTs [%] on active",
xaxisround=1)
}
}else{
if("propDLTs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLTs * 100,
description="Proportion of DLTs [%]",
xaxisround=1)
}
}
## distribution of number of patients treated at too much tox
if("nAboveTarget" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nAboveTarget,
description="Number of patients above target")
}
## first combine these small plots
if(length(plotList))
{
ret <-
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just use that
plotList[[1L]]
} else {
## multiple plots in this case
do.call(gridExtra::arrangeGrob,
plotList)
}
}
## then return
ret
})
##' Plot summaries of the model-based design simulations
##'
##' Graphical display of the simulation summary
##'
##' This plot method can be applied to \code{\linkS4class{SimulationsSummary}}
##' objects in order to summarize them graphically. Possible \code{type} of
##' plots at the moment are those listed in
##' \code{\link{plot,GeneralSimulationsSummary,missing-method}} plus:
##' \describe{
##' \item{meanFit}{Plot showing the average fitted dose-toxicity curve across
##' the trials, together with 95% credible intervals, and comparison with the
##' assumed truth (as specified by the \code{truth} argument to
##' \code{\link{summary,Simulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{SimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-SimulationsSummary.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="SimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget",
"meanFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for model-based
## designs
typeReduced <- setdiff(type,
"meanFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanFit plot requested?
if("meanFit" %in% type)
{
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity",
"95% interval for estimated toxicity")
## create the data frame, with
## true tox, average estimated tox, and 95% (lower, upper)
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1,
"95% interval for estimated toxicity"=2)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2,
"95% interval for estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLT [%]")
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot)
else
thisPlot
}
## then finally plot everything
ret
})
##' Plot summaries of the dual-endpoint design simulations
##'
##' This plot method can be applied to \code{\linkS4class{DualSimulationsSummary}}
##' objects in order to summarize them graphically. Possible \code{type} of
##' plots at the moment are those listed in
##' \code{\link{plot,SimulationsSummary,missing-method}} plus:
##' \describe{
##' \item{meanBiomarkerFit}{Plot showing the average fitted dose-biomarker curve across
##' the trials, together with 95% credible intervals, and comparison with the
##' assumed truth (as specified by the \code{trueBiomarker} argument to
##' \code{\link{summary,DualSimulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{DualSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-DualSimulationsSummary.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="DualSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget",
"meanFit",
"meanBiomarkerFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for dual-endpoint
## designs
typeReduced <- setdiff(type,
"meanBiomarkerFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanBiomarkerFit plot requested?
if("meanBiomarkerFit" %in% type)
{
## which types of lines do we have?
linetype <- c("True biomarker",
"Average estimated biomarker",
"95% interval for estimated biomarker")
## create the data frame, with
## true biomarker, average estimated biomarker, and 95% (lower, upper)
## estimated biomarker stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanBiomarkerFit))
## linetypes for the plot
lt <- c("True biomarker"=1,
"Average estimated biomarker"=1,
"95% interval for estimated biomarker"=2)
## colour for the plot
col <- c("True biomarker"=1,
"Average estimated biomarker"=2,
"95% interval for estimated biomarker"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Biomarker level")
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot, heights=c(2/3, 1/3))
else
thisPlot
}
## then finally plot everything
ret
})
## --------------------------------------------------------------------------------------------------------
##' Summarize the simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{PseudoSimulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param targetEndOfTrial the target probability of DLE wanted to achieve at the end of a trial
##' @param targetDuringTrial the target probability of DLE wanted to achieve during a trial
##'
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{PseudoSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMsingle.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoSimulations"),
def=
function(object,
truth,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##extract dose grid
doseGrid <- object@data[[1]]@doseGrid
##evaluate true DLE at doseGrid
trueDLE <- truth(doseGrid)
##Inverse function of the truth function
inverse = function (f, lower = -100, upper = 100) {
function (y) uniroot((function (x) f(x) - y), lower = lower, upper = upper)[1]
}
##Function to obtain corresponsing dose level given target prob
TD <- inverse(truth, 0, max(doseGrid))
##Find the dose corresponding to the target dose during trial
targetDoseEndOfTrial <- as.numeric(TD(targetEndOfTrial))
##Find the dose corresponding to the target does end of trial
targetDoseDuringTrial <- as.numeric(TD(targetDuringTrial))
##Find the dose at doseGrid corresponding to the above two quantities
targetDoseEndOfTrialAtDoseGrid <- doseGrid[max(which(targetDoseEndOfTrial-doseGrid >= 0))]
targetDoseDuringTrialAtDoseGrid <- doseGrid[max(which(targetDoseDuringTrial-doseGrid >= 0))]
##A summary for all TDtargetEndOfTrial dose obtained
TDEOTSummary <- summary(object@FinalTDtargetEndOfTrialEstimates)
FinalDoseRecSummary <- TDEOTSummary
ratioTDEOTSummary <- summary(object@FinalTDEOTRatios)
FinalRatioSummary <- ratioTDEOTSummary
##A summary for all TDtargetDuringTrial dose obtained
TDDTSummary <- summary(object@FinalTDtargetDuringTrialEstimates)
##what are the levels above target End of Trial?
xAboveTargetEndOfTrial <- which(trueDLE > targetEndOfTrial)
##what are the levels above target During Trial?
xAboveTargetDuringTrial<- which(trueDLE > targetDuringTrial)
##proportion of DLEs in this trial
propDLE<- sapply(object@data,
function(d) {
mean(d@y)
})
### mean toxicity risk
meanToxRisk <- sapply(object@data,
function(d){
mean(trueDLE[d@xLevel])
})
## doses selected for MTD
doseSelected <- object@doses
## replace NA by 0
doseSelected[is.na(doseSelected)] <- 0
## dose most often selected as MTD
doseMostSelected <-
as.numeric(names(which.max(table(doseSelected))))
#doseRec <- doseMostSelected
xMostSelected <-
matchTolerance(doseMostSelected,
table=doseGrid)
## observed toxicity rate at dose most often selected
## Note: this does not seem very useful!
## Reason: In case of a fine grid, few patients if any
## will have been treated at this dose.
tmp <-
sapply(object@data,
function(d){
whichAtThisDose <- which(d@x == doseMostSelected)
nAtThisDose <- length(whichAtThisDose)
nDLTatThisDose <- sum(d@y[whichAtThisDose])
return(c(nAtThisDose=nAtThisDose,
nDLTatThisDose=nDLTatThisDose))
})
obsToxRateAtDoseMostSelected <-
mean(tmp["nDLTatThisDose",]) / mean(tmp["nAtThisDose",])
## number of patients overall
nObs <- sapply(object@data,
slot,
"nObs")
## number of patients treated above target End of trial
nAboveTargetEndOfTrial <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTargetEndOfTrial)
})
## number of patients treated above target During trial
nAboveTargetDuringTrial <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTargetDuringTrial)
})
toxAtDoses <- truth(doseSelected)
## Proportion of trials selecting target TDEndOfTrial and TDDuringTrial
nsim <- length(object@data)
propAtTargetEndOfTrial <- (length(which(object@doses==targetDoseEndOfTrialAtDoseGrid)))/nsim
propAtTargetDuringTrial <- (length(which(object@doses==targetDoseDuringTrialAtDoseGrid)))/nsim
RecDoseSummary <- TDEOTSummary
##fitted probDLE at dose most often selected
##find names in the fit list (check it is with or without samples)
FitNames<- sapply(object@fit,names)
if ("probDLE" %in% FitNames){
fitAtDoseMostSelected <- sapply(object@fit,
function(f){
f$probDLE[xMostSelected]
})
meanFitMatrix <- sapply(object@fit,
"[[",
"probDLE")
meanFit <- list(truth=
truth(doseGrid),
average=rowMeans(meanFitMatrix))
} else {
## fitted toxicity rate at dose most often selected
fitAtDoseMostSelected <-
sapply(object@fit,
function(f){
f$middle[xMostSelected]
})
## mean fitted toxicity (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanFitMatrix <- sapply(object@fit,
"[[",
"middle")
meanFit <- list(truth=
truth(doseGrid),
average=
rowMeans(meanFitMatrix),
lower=
apply(meanFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanFitMatrix,
1L,
quantile,
0.975))
}
## give back an object of class GeneralSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoSimulationsSummary(
targetEndOfTrial=targetEndOfTrial,
targetDoseEndOfTrial=targetDoseEndOfTrial,
targetDuringTrial=targetDuringTrial,
targetDoseDuringTrial=targetDoseDuringTrial,
targetDoseEndOfTrialAtDoseGrid=targetDoseEndOfTrialAtDoseGrid,
targetDoseDuringTrialAtDoseGrid=targetDoseDuringTrialAtDoseGrid,
TDEOTSummary=TDEOTSummary,
TDDTSummary=TDDTSummary,
FinalDoseRecSummary=FinalDoseRecSummary,
ratioTDEOTSummary=ratioTDEOTSummary,
FinalRatioSummary=FinalRatioSummary,
nsim=length(object@data),
propDLE=propDLE,
meanToxRisk=meanToxRisk,
doseSelected=doseSelected,
doseMostSelected=doseMostSelected,
#doseRec=doseRec,
obsToxRateAtDoseMostSelected=obsToxRateAtDoseMostSelected,
nObs=nObs,
nAboveTargetEndOfTrial=nAboveTargetEndOfTrial,
nAboveTargetDuringTrial=nAboveTargetDuringTrial,
toxAtDosesSelected=toxAtDoses,
propAtTargetEndOfTrial=propAtTargetEndOfTrial,
propAtTargetDuringTrial=propAtTargetDuringTrial,
doseGrid=doseGrid,
fitAtDoseMostSelected=fitAtDoseMostSelected,
meanFit=meanFit)
return(ret)
})
## ========================================================================================================
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{PseudoSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-showSIMsingle.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="PseudoSimulationsSummary"),
def=
function(object){
r <- Report$new(object=object,
df=
as.data.frame(matrix(nrow=1,
ncol=0)),
dfNames=character())
cat("Summary of",
r$dfSave(object@nsim, "nsim"),
"simulations\n\n")
cat("Target probability of DLE p(DLE) used at the end of a trial was",
r$dfSave(object@targetEndOfTrial * 100,
"targetEndOfTrial"),"%\n")
cat("The dose level corresponds to the target p(DLE) used at the end of a trial, TDEOT, was",
r$dfSave(object@targetDoseEndOfTrial,
"targetDoseEndOfTrial"),"\n")
cat("TDEOT at dose Grid was",
r$dfSave(object@targetDoseEndOfTrialAtDoseGrid,
"targetDoseEndOfTrialAtDoseGrid"),"\n")
cat("Target p(DLE) used during a trial was",
r$dfSave(object@targetDuringTrial * 100,
"targetDuringTrial"),"%\n")
cat("The dose level corresponds to the target p(DLE) used during a trial, TDDT, was",
r$dfSave(object@targetDoseDuringTrial,
"targetDoseDuringTrial"),"\n")
cat("TDDT at dose Grid was",
r$dfSave(object@targetDoseDuringTrialAtDoseGrid,
"targetDoseDuringTrialAtDoseGrid"),"\n")
r$report("nObs",
"Number of patients overall",
percent=FALSE)
r$report("nAboveTargetEndOfTrial",
"Number of patients treated above the target p(DLE) used at the end of a trial",
percent=FALSE)
r$report("nAboveTargetDuringTrial",
"Number of patients treated above the target p(DLE) used during a trial",
percent=FALSE)
r$report("propDLE",
"Proportions of observed DLT in the trials")
r$report("meanToxRisk",
"Mean toxicity risks for the patients")
r$report("doseSelected",
"Doses selected as TDEOT",
percent=FALSE, digits=1)
#r$report("doseRec",
# "Doses to recommend to subsequent study",
# percent=FALSE, digits=1)
r$report("toxAtDosesSelected",
"True toxicity at TDEOT")
cat("Proportion of trials selecting the TDEOT:",
r$dfSave(object@propAtTargetEndOfTrial * 100,
"percentAtTarget"),
"%\n")
cat("Proportion of trials selecting the TDDT:",
r$dfSave(object@propAtTargetDuringTrial * 100,
"percentAtTarget"),
"%\n")
cat("Dose most often selected as TDEOT:",
r$dfSave(object@doseMostSelected,
"doseMostSelected"),
"\n")
cat("Observed toxicity rate at dose most often selected:",
r$dfSave(round(object@obsToxRateAtDoseMostSelected * 100),
"obsToxRateAtDoseMostSelected"),
"%\n")
r$report("fitAtDoseMostSelected",
"Fitted probabilities of DLE at dose most often selected")
TDEOTSum <- object@TDEOTSummary
r$dfSave(as.numeric(TDEOTSum[1]),"TDEOTMin")
r$dfSave(as.numeric(TDEOTSum[2]),"TDEOTlower")
r$dfSave(as.numeric(TDEOTSum[3]),"TDEOTMedian")
r$dfSave(as.numeric(TDEOTSum[4]),"TDEOTMean")
r$dfSave(as.numeric(TDEOTSum[5]),"TDEOTUpper")
r$dfSave(as.numeric(TDEOTSum[6]),"TDEOTMax")
cat("The summary table of the final TDEOT across all simulations\n",
capture.output(TDEOTSum)[1],"\n",
capture.output(TDEOTSum)[2],"\n")
ratioTDEOTSum <-object@ratioTDEOTSummary
r$dfSave(as.numeric(ratioTDEOTSum[1]),"ratioTDEOTMin")
r$dfSave(as.numeric(ratioTDEOTSum[2]),"ratioTDEOTlower")
r$dfSave(as.numeric(ratioTDEOTSum[3]),"ratioTDEOTMedian")
r$dfSave(as.numeric(ratioTDEOTSum[4]),"ratioTDEOTMean")
r$dfSave(as.numeric(ratioTDEOTSum[5]),"ratioTDEOTUpper")
r$dfSave(as.numeric(ratioTDEOTSum[6]),"ratioTDEOTMax")
cat("The summary table of the final ratios of the TDEOT across all simulations\n",
capture.output(ratioTDEOTSum)[1],"\n",
capture.output(ratioTDEOTSum)[2],"\n")
TDDTSum <- object@TDDTSummary
r$dfSave(as.numeric(TDDTSum[1]),"TDDTMin")
r$dfSave(as.numeric(TDDTSum[2]),"TDDTlower")
r$dfSave(as.numeric(TDDTSum[3]),"TDDTMedian")
r$dfSave(as.numeric(TDDTSum[4]),"TDDTMean")
r$dfSave(as.numeric(TDDTSum[5]),"TDDTUpper")
r$dfSave(as.numeric(TDDTSum[6]),"TDDTMax")
cat("The summary table of the final TDDT across all simulations\n",
capture.output(TDDTSum)[1],"\n",
capture.output(TDDTSum)[2],"\n")
FinalDoseRecSum <- object@FinalDoseRecSummary
r$dfSave(as.numeric(FinalDoseRecSum[1]),"FinalDoseRecMin")
r$dfSave(as.numeric(FinalDoseRecSum[2]),"FinalDoseReclower")
r$dfSave(as.numeric(FinalDoseRecSum[3]),"FinalDoseRecMedian")
r$dfSave(as.numeric(FinalDoseRecSum[4]),"FinalDoseRecMean")
r$dfSave(as.numeric(FinalDoseRecSum[5]),"FinalDoseRecUpper")
r$dfSave(as.numeric(FinalDoseRecSum[6]),"FinalDoseRecMax")
cat("The summary table of dose levels, the optimal dose\n to recommend for subsequent study across all simulations\n",
capture.output(FinalDoseRecSum)[1],"\n",
capture.output(FinalDoseRecSum)[2],"\n")
FinalratioSum <-object@FinalRatioSummary
r$dfSave(as.numeric(FinalratioSum[1]),"FinalratioMin")
r$dfSave(as.numeric(FinalratioSum[2]),"Finalratiolower")
r$dfSave(as.numeric(FinalratioSum[3]),"FinalratioMedian")
r$dfSave(as.numeric(FinalratioSum[4]),"FinalratioMean")
r$dfSave(as.numeric(FinalratioSum[5]),"FinalratioUpper")
r$dfSave(as.numeric(FinalratioSum[6]),"FinalratioMax")
cat("The summary table of the final ratios of the optimal dose for stopping across
all simulations\n",
capture.output(FinalratioSum)[1],"\n",
capture.output(FinalratioSum)[2],"\n\n")
## finally assign names to the df
## and return it invisibly
names(r$df) <- r$dfNames
invisible(r$df)
})
## -------------------------------------------------------------------------------------------
##' Plot summaries of the pseudo simulations
##'
##' Graphical display of the simulation summary
##'
##' This plot method can be applied to \code{\linkS4class{PseudoSimulationsSummary}}
##' objects in order to summarize them graphically. This can be used when only DLE responses are involved
##' in the simulations. This also applied to results with or without samples generated during the simulations
##'
##' @param x the \code{\linkS4class{PseudoSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSUMsingle.R
##' @export
##' @keywords methods
##'
setMethod("plot",
signature=
signature(x="PseudoSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLE",
"nAboveTargetEndOfTrial",
"meanFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## distribution of overall sample size
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs,
description="Number of patients in total")
}
## distribution of final MTD estimate
if("doseSelected" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@doseSelected,
description="MTD estimate")
}
## distribution of proportion of DLTs
if("propDLE" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLE * 100,
description="Proportion of DLE [%]",
xaxisround=1)
}
## distribution of number of patients treated at too much tox
if("nAboveTargetEndOfTrial" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nAboveTargetEndOfTrial,
description="Number of patients above target")
}
## first combine these small plots
if(length(plotList))
{
ret <-
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just use that
plotList[[1L]]
} else {
## multiple plots in this case
do.call(gridExtra::arrangeGrob,
plotList)
}
}
##the meanFit plot
if ("meanFit" %in% type)
{## Find if DLE samples are generated in the simulations
## by checking if there the lower limits of the 95% Credibility
## interval are calculated
if (!is.null(x@meanFit$lower)) {
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity",
"95% interval for estimated toxicity")
## create the data frame, with
## true tox, average estimated tox, and 95% (lower, upper)
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1,
"95% interval for estimated toxicity"=2)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2,
"95% interval for estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLE [%]")} else {
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity")
## create the data frame, with
## true tox, average estimated tox
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 2L),
group=
rep(1:2, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLE [%]")}
}
## then add this plot at the bottom
ret <- gridExtra::arrangeGrob(ret,thisPlot)
ret
})
## --------------------------------------------------------------------------------------
##' Plot simulations
##'
##' Summarize the simulations with plots
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} \item{sigma2}{The variance of the efficacy responses}}
##' You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMDual.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,
"sigma2")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## Efficacy variance estimates boxplot
if("sigma2" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2est), geom="boxplot",
xlab="", ylab="Efficacy variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret,heights=c(2/3, 1/3))
}
return(ret)
})
## ---------------------------------------------------------------------------------
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualFlexiSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} \item{sigma2}{The variance of the efficacy responses}
##' \item{sigma2betaW}{The variance of the random walk model}}
##' You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualFlexiSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMDualFlexi.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualFlexiSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2",
"sigma2betaW"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,"sigma2betaW")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## random walk model variance estimates boxplot
if("sigma2betaW" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2betaWest), geom="boxplot",
xlab="", ylab="Random walk model variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret,heights=c(2/3, 1/3))
}
return(ret)
})
## -----------------------------------------------------------------------------------------
##' Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model
##' (except the EffFlexi class model)
##'
##' @param object the \code{\linkS4class{PseudoDualSimulations}} object we want to summarize
##' @param trueDLE a function which takes as input a dose (vector) and returns the true probability (vector)
##' of DLE
##' @param trueEff a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).
##' @param targetEndOfTrial the target probability of DLE that are used at the end of a trial. Default at 0.3.
##' @param targetDuringTrial the target probability of DLE that are used during the trial. Default at 0.35.
##' @param \dots Additional arguments can be supplied here for \code{trueDLE} and \code{trueEff}
##' @return an object of class \code{\linkS4class{PseudoDualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMDual.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoDualSimulations"),
def=
function(object,
trueDLE,
trueEff,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##call the parent method
start <- callNextMethod(object=object,
truth=trueDLE,
targetEndOfTrial=targetEndOfTrial,
targetDuringTrial=targetDuringTrial,
...)
doseGrid <- object@data[[1]]@doseGrid
## ## dose level most often selected as MTD (TDtargetEnd of Trial)
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
##check if true Eff is a function
## check if special case applies
isTrueEffFx <- is.function(trueEff)
TDtargetEndOfTrial <- start@targetDoseEndOfTrial
if (isTrueEffFx) {
negtrueGainfn <- function(dose)
{return(-(trueEff(dose))/(1+(trueDLE(dose)/(1-trueDLE(dose)))))}
Gstar <- optim(exp(1),negtrueGainfn,method="BFGS")$par
maxGainValue <- -(optim(exp(1),negtrueGainfn,method="BFGS")$value)
GstarAtDoseGrid <- doseGrid[max(which(Gstar-doseGrid >= 0 ))]
} else {
trueGain <- (trueEff)/(1+(trueDLE(doseGrid)/(1-trueDLE(doseGrid))))
maxGainValue<-max(trueGain)
Gstar <- doseGrid[which.max(trueGain)]
GstarAtDoseGrid <- Gstar
}
##A summary for all final Gstar obtained
GstarSummary <- summary(object@FinalGstarEstimates)
ratioGstarSummary <- summary(object@FinalGstarRatios)
FinalDoseRecSummary <- summary(object@FinalOptimalDose)
FinalRatioSummary <- summary(object@FinalRatios)
##find names in the fit efficacy list (check it is with or without samples)
FitNames<- sapply(object@fitEff,names)
if ("ExpEff" %in% FitNames){
## fitted efficacy level at dose most often selected
EffFitAtDoseMostSelected <- sapply(object@fitEff,
function(f){
f$ExpEff[xMostSelected]
})
meanEffFitMatrix <- sapply(object@fitEff,
"[[",
"ExpEff")
meanEffFit <- list(truth=
trueEff(doseGrid),
average=rowMeans(meanEffFitMatrix))
} else {## fitted efficacy level at dose most often selected
EffFitAtDoseMostSelected <-
sapply(object@fitEff,
function(f){
f$middle[xMostSelected]
})
## mean fitted curve (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanEffFitMatrix <- sapply(object@fitEff,
"[[",
"middle")
## check if special case applies
if (isTrueEffFx) {TRUTHeff<- trueEff(doseGrid)} else {TRUTHeff <- trueEff}
meanEffFit <- list(truth=
TRUTHeff,
average=
rowMeans(meanEffFitMatrix),
lower=
apply(meanEffFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanEffFitMatrix,
1L,
quantile,
0.975))}
## give back an object of class PseudoDualSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoDualSimulationsSummary(
start,
targetGstar=Gstar,
targetGstarAtDoseGrid=GstarAtDoseGrid,
GstarSummary=GstarSummary,
ratioGstarSummary=ratioGstarSummary,
FinalDoseRecSummary=FinalDoseRecSummary,
FinalRatioSummary=FinalRatioSummary,
EffFitAtDoseMostSelected=EffFitAtDoseMostSelected,
meanEffFit=meanEffFit)
return(ret)
})
## --------------------------------------------------------------------------------------------------
##' Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.
##'
##' @param object the \code{\linkS4class{PseudoDualFlexiSimulations}} object we want to summarize
##' @param trueDLE a function which takes as input a dose (vector) and returns the true probability of DLE (vector)
##' @param trueEff a vector which takes as input the true mean efficacy values at all dose levels (in order)
##' @param targetEndOfTrial the target probability of DLE that are used at the end of a trial. Default at 0.3.
##' @param targetDuringTrial the target probability of DLE that are used during the trial. Default at 0.35.
##' @param \dots Additional arguments can be supplied here for \code{trueDLE} and \code{trueEff}
##' @return an object of class \code{\linkS4class{PseudoDualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMDualFlexi.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoDualFlexiSimulations"),
def=
function(object,
trueDLE,
trueEff,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##call the parent method
start <- callNextMethod(object=object,
trueDLE=trueDLE,
trueEff=trueEff,
targetEndOfTrial=targetEndOfTrial,
targetDuringTrial=targetDuringTrial,
...)
## give back an object of class PseudoDualSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoDualSimulationsSummary(start)
return(ret)
})
## ----------------------------------------------------------------------------------------
##' Show the summary of Pseudo Dual simulations summary
##'
##' @param object the \code{\linkS4class{PseudoDualSimulationsSummary}} object we want to print
##' @return invisibly returns a data frame of the results with one row and appropriate column names
##'
##'
##' @example examples/Simulations-method-showSIMDual.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="PseudoDualSimulationsSummary"),
def=
function(object){
##call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
##start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
##add three reporting lines
cat("Target Gstar, the dose which gives the maximum gain value was",
r$dfSave(object@targetGstar,
"targetGstar"),"\n")
cat("Target Gstar at dose Grid was",
r$dfSave(object@targetGstarAtDoseGrid,
"targetGstarAtDoseGrid"),"\n")
GstarSum <- object@GstarSummary
r$dfSave(as.numeric(GstarSum[1]),"GstarMin")
r$dfSave(as.numeric(GstarSum[2]),"Gstarlower")
r$dfSave(as.numeric(GstarSum[3]),"GstarMedian")
r$dfSave(as.numeric(GstarSum[4]),"GstarMean")
r$dfSave(as.numeric(GstarSum[5]),"GstarUpper")
r$dfSave(as.numeric(GstarSum[6]),"GstarMax")
cat("The summary table of the final Gstar across all simulations\n",
capture.output(GstarSum)[1],"\n",
capture.output(GstarSum)[2],"\n")
ratioGstarSum <-object@ratioGstarSummary
r$dfSave(as.numeric(ratioGstarSum[1]),"ratioGstarMin")
r$dfSave(as.numeric(ratioGstarSum[2]),"ratioGstarlower")
r$dfSave(as.numeric(ratioGstarSum[3]),"ratioGstarMedian")
r$dfSave(as.numeric(ratioGstarSum[4]),"ratioGstarMean")
r$dfSave(as.numeric(ratioGstarSum[5]),"ratioGstarUpper")
r$dfSave(as.numeric(ratioGstarSum[6]),"ratioGstarMax")
cat("The summary table of the final ratios of the Gstar across all simulations\n",
capture.output(ratioGstarSum)[1],"\n",
capture.output(ratioGstarSum)[2],"\n")
FinalDoseRecSum <- object@FinalDoseRecSummary
r$dfSave(as.numeric(FinalDoseRecSum[1]),"FinalDoseRecMin")
r$dfSave(as.numeric(FinalDoseRecSum[2]),"FinalDoseReclower")
r$dfSave(as.numeric(FinalDoseRecSum[3]),"FinalDoseRecMedian")
r$dfSave(as.numeric(FinalDoseRecSum[4]),"FinalDoseRecMean")
r$dfSave(as.numeric(FinalDoseRecSum[5]),"FinalDoseRecUpper")
r$dfSave(as.numeric(FinalDoseRecSum[6]),"FinalDoseRecMax")
cat("The summary table of dose levels, the optimal dose\n to recommend for subsequent study across all simulations\n",
capture.output(FinalDoseRecSum)[1],"\n",
capture.output(FinalDoseRecSum)[2],"\n")
FinalratioSum <-object@FinalRatioSummary
r$dfSave(as.numeric(FinalratioSum[1]),"FinalratioMin")
r$dfSave(as.numeric(FinalratioSum[2]),"Finalratiolower")
r$dfSave(as.numeric(FinalratioSum[3]),"FinalratioMedian")
r$dfSave(as.numeric(FinalratioSum[4]),"FinalratioMean")
r$dfSave(as.numeric(FinalratioSum[5]),"FinalratioUpper")
r$dfSave(as.numeric(FinalratioSum[6]),"FinalratioMax")
cat("The summary table of the final ratios of the optimal dose for stopping across
all simulations\n",
capture.output(FinalratioSum)[1],"\n",
capture.output(FinalratioSum)[2],"\n")
r$report("EffFitAtDoseMostSelected",
"Fitted expected efficacy level at dose most often selected",
percent=FALSE,
digits=1)
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
## --------------------------------------------------------------------------------------------------
##' Plot the summary of Pseudo Dual Simulations summary
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualSimulationsSummary}} objects in order
##' to summarize them graphically. Possible \code{type} of plots at the moment are those listed in
##' \code{\link{plot,PseudoSimulationsSummary,missing-method}} plus:
##' \describe{\item{meanEffFit}{Plot showing the fitted dose-efficacy curve. If no samples are involved, only the
##' average fitted dose-efficacy curve across the trials will be plotted. If samples (DLE and efficacy) are involved,
##' the average fitted dose-efficacy curve across the trials, together with the 95% credibility interval; and comparison
##' with the assumed truth (as specified by the \code{trueEff} argument to
##' \code{\link{summary,PseudoDualSimulations-method}})}}
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualSimulationsSummary}} object we want to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSUMDual.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLE",
"nAboveTargetEndOfTrial",
"meanFit",
"meanEffFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for dual-endpoint
## designs
typeReduced <- setdiff(type,
"meanEffFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanBiomarkerFit plot requested?
if("meanEffFit" %in% type)
{ ## Find if Effsamples are generated in the simulations
## by checking if there the lower limits of the 95% Credibility
## interval are calculated
if (!is.null(x@meanEffFit$lower)) {
## which types of lines do we have?
linetype <- c("True Expected Efficacy",
"Average estimated expected efficacy",
"95% interval for estimated expected efficacy")
## create the data frame, with
## true biomarker, average estimated expected efficacy, and 95% (lower, upper)
## estimated biomarker stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanEffFit))
## linetypes for the plot
lt <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=1,
"95% interval for estimated expected efficacy"=2)
## colour for the plot
col <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=4,
"95% interval for estimated expected efficacy"=4)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Expected Efficacy level")} else {
linetype <- c("True Expected Efficacy",
"Average estimated expected efficacy")
## create the data frame, with
## true biomarker, average estimated expected efficacy
dat <- data.frame(dose=
rep(x@doseGrid, 2L),
group=
rep(1:2, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanEffFit))
## linetypes for the plot
lt <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=1)
## colour for the plot
col <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=4)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Expected Efficacy level")
}
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot, heights=c(2/3, 1/3))
else
thisPlot
}
## then finally plot everything
ret
})
## ------------------------------------------------------------------------
0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.
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#####################################################################################
## Author: Daniel Sabanes Bove [sabanesd *a*t* roche *.* com],
## Wai Yin Yeung [ w *.* yeung1 *a*t* lancaster *.* ac *.* uk]
## Project: Object-oriented implementation of CRM designs
##
## Time-stamp: <[Simulations-methods.R] by DSB Fre 16/01/2015 13:41>
##
## Description:
## Methods for handling the simulations output.
##
## History:
## 19/02/2014 file creation
## 30/07/2014 added in methods for pseudo models simulations
###################################################################################
##' @include Simulations-class.R
##' @include helpers.R
{}
##' Plot simulations
##'
##' Summarize the simulations with plots
##'
##' This plot method can be applied to \code{\linkS4class{GeneralSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} } You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{GeneralSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMsingle.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="GeneralSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## summary of the trajectories
if("trajectory" %in% type)
{
## get a matrix of the simulated dose trajectories,
## where the rows correspond to the simulations and
## the columns to the patient index:
## If design with placebo, then exclude placebo Patients
if(x@data[[1]]@placebo){
PL <- x@data[[1]]@doseGrid[1]
simDoses <- lapply(x@data,
function(y){ y@x[y@x != PL]})
}else{
simDoses <- lapply(x@data,
slot,
"x")
}
maxPatients <- max(sapply(simDoses, length))
simDosesMat <- matrix(data=NA,
nrow=length(simDoses),
ncol=maxPatients)
for(i in seq_along(simDoses))
{
simDosesMat[i, seq_along(simDoses[[i]])] <-
simDoses[[i]]
}
## extract statistics
stats <- c("Minimum",
"Lower Quartile",
"Median",
"Upper Quartile",
"Maximum")
traj.df <-
data.frame(patient=
rep(seq_len(maxPatients), each=5L),
Statistic=
factor(rep(stats,
maxPatients),
levels=stats),
traj=
c(apply(simDosesMat, 2L, quantile,
na.rm=TRUE)))
## linetypes for the plot
lt <- c("Median" = 1,
"Lower Quartile" = 2,
"Upper Quartile" = 2,
"Minimum" = 4,
"Maximum"= 4)
## save the plot
if(x@data[[1]]@placebo){
myTitle <- "Patient (placebo were excluded)"
}else{
myTitle <- "Patient"
}
plotList[[plotIndex <- plotIndex + 1L]] <-
ggplot() +
geom_step(aes(x=patient,
y=traj,
group=Statistic,
linetype=Statistic),
size=1.2, colour="blue", data=traj.df) +
## scale_linetype_manual(values=lt) +
xlab(myTitle) + ylab("Dose Level")
}
## average distribution of the doses tried
if("dosesTried" %in% type)
{
## get the doses tried
simDoses <- lapply(x@data,
slot,
"x")
## get the dose distributions by trial
doseDistributions <-
sapply(simDoses,
function(s){
prop.table(table(factor(s,
levels=
x@data[[1]]@doseGrid)))
})
## derive the average dose distribution across trial
## simulations
averageDoseDist <- rowMeans(doseDistributions)
## get in data frame shape
dat <- data.frame(dose=as.numeric(names(averageDoseDist)),
perc=averageDoseDist * 100)
## produce and save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
ggplot() +
geom_bar(data=as.data.frame(dat),
aes(x=dose, y=perc),
stat="identity",
position="identity",
width=min(diff(x@data[[1]]@doseGrid)) / 2) +
xlab("Dose level") +
ylab("Average proportion [%]")
}
## then finally plot everything
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just return it
return(plotList[[1L]])
} else {
## otherwise arrange them
ret <- do.call(gridExtra::arrangeGrob,
plotList)
return(ret)
}
})
##' Plot dual-endpoint simulations
##'
##' This plot method can be applied to \code{\linkS4class{DualSimulations}}
##' objects in order to summarize them graphically. In addition to the standard
##' plot types, there is
##' \describe{
##' \item{sigma2W}{Plot a boxplot of the final biomarker variance estimates in
##' the simulated trials}
##' \item{rho}{Plot a boxplot of the final correlation estimates in
##' the simulated trials}
##' }
##'
##' @param x the \code{\linkS4class{DualSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 qplot coord_flip scale_x_discrete
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-DualSimulations.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="DualSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2W",
"rho"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,
c("sigma2W", "rho"))
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## biomarker variance estimates boxplot
if("sigma2W" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2West), geom="boxplot",
xlab="", ylab="Biomarker variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## correlation estimates boxplot
if("rho" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@rhoEst), geom="boxplot",
xlab="", ylab="Correlation estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret)
}
return(ret)
})
##' Summarize the simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{GeneralSimulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{GeneralSimulationsSummary}}
##'
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="GeneralSimulations"),
def=
function(object,
truth,
target=c(0.2, 0.35),
...){
## extract dose grid
doseGrid <- object@data[[1]]@doseGrid
## evaluate true toxicity at doseGrid
trueTox <- truth(doseGrid, ...)
## find dose interval corresponding to target tox interval
targetDoseInterval <-
sapply(target,
function(t){
## we have to be careful because it might be
## that in the range of the dose grid, no
## doses can be found that match the target
## interval boundaries!
## In that case we want to return NA
r <- try(uniroot(f=function(x){truth(x, ...) - t},
interval=
range(doseGrid))$root,
silent=TRUE)
if(inherits(r, "try-error"))
{
return(NA_real_)
} else {
return(r)
}
})
## what are the levels above target interval?
xAboveTarget <- which(trueTox > target[2])
## proportion of DLTs in a trial:
if(object@data[[1]]@placebo){
if( sum(object@data[[1]]@x == doseGrid[1]) ){
propDLTs <- sapply(object@data,
function(d){
tapply(d@y,
factor(d@x == d@doseGrid[1],
labels=c("ACTV","PLCB")),
mean)
})
}else{
propDLTs <- sapply(object@data,
function(d){
c('ACTV' = mean(d@y),'PLCB' = NA)
})
}
}else{
propDLTs <- sapply(object@data,
function(d){
mean(d@y)
})
}
## mean toxicity risk
if(object@data[[1]]@placebo){
meanToxRisk <- sapply(object@data,
function(d){
mean(trueTox[d@xLevel[d@xLevel != 1]])
})
}else{
meanToxRisk <- sapply(object@data,
function(d){
mean(trueTox[d@xLevel])
})
}
## doses selected for MTD
doseSelected <- object@doses
## replace NA by 0
doseSelected[is.na(doseSelected)] <- 0
## dose most often selected as MTD
doseMostSelected <-
as.numeric(names(which.max(table(doseSelected))))
xMostSelected <-
matchTolerance(doseMostSelected,
table=doseGrid)
## observed toxicity rate at dose most often selected
## Note: this does not seem very useful!
## Reason: In case of a fine grid, few patients if any
## will have been treated at this dose.
tmp <-
sapply(object@data,
function(d){
whichAtThisDose <- which(d@x == doseMostSelected)
nAtThisDose <- length(whichAtThisDose)
nDLTatThisDose <- sum(d@y[whichAtThisDose])
return(c(nAtThisDose=nAtThisDose,
nDLTatThisDose=nDLTatThisDose))
})
obsToxRateAtDoseMostSelected <-
mean(tmp["nDLTatThisDose",]) / mean(tmp["nAtThisDose",])
## number of patients overall
if(object@data[[1]]@placebo){
nObs <- sapply(object@data,
function(x){
data.frame(n.ACTV = sum(x@xLevel != 1L),
n.PLCB = sum(x@xLevel == 1L))
})
nObs <- matrix(unlist(nObs), dim(nObs))
}else{
nObs <- sapply(object@data,
slot,
"nObs")
}
## number of patients treated above target tox interval
nAboveTarget <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTarget)
})
## Proportion of trials selecting target MTD
toxAtDoses <- truth(doseSelected, ...)
propAtTarget <- mean((toxAtDoses > target[1]) &
(toxAtDoses < target[2]))
## give back an object of class GeneralSimulationsSummary,
## for which we then define a print / plot method
ret <-
.GeneralSimulationsSummary(
target=target,
targetDoseInterval=targetDoseInterval,
nsim=length(object@data),
propDLTs=propDLTs,
meanToxRisk=meanToxRisk,
doseSelected=doseSelected,
doseMostSelected=doseMostSelected,
obsToxRateAtDoseMostSelected=obsToxRateAtDoseMostSelected,
nObs=nObs,
nAboveTarget=nAboveTarget,
toxAtDosesSelected=toxAtDoses,
propAtTarget=propAtTarget,
doseGrid=doseGrid,
placebo=object@data[[1]]@placebo)
return(ret)
})
##' Summarize the model-based design simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{Simulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{SimulationsSummary}}
##'
##' @example examples/Simulations-method-summary.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="Simulations"),
def=
function(object,
truth,
target=c(0.2, 0.35),
...){
## call the parent method
start <- callNextMethod(object=object,
truth=truth,
target=target,
...)
doseGrid <- object@data[[1]]@doseGrid
## dose level most often selected as MTD
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
## fitted toxicity rate at dose most often selected
fitAtDoseMostSelected <-
sapply(object@fit,
function(f){
f$middle[xMostSelected]
})
## mean fitted toxicity (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanFitMatrix <- sapply(object@fit,
"[[",
"middle")
meanFit <- list(truth=
truth(doseGrid, ...),
average=
rowMeans(meanFitMatrix),
lower=
apply(meanFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanFitMatrix,
1L,
quantile,
0.975))
## give back an object of class SimulationsSummary,
## for which we then define a print / plot method
ret <- .SimulationsSummary(
start,
fitAtDoseMostSelected=fitAtDoseMostSelected,
meanFit=meanFit)
return(ret)
})
##' Summarize the dual-endpoint design simulations, relative to given true
##' dose-toxicity and dose-biomarker curves
##'
##' @param object the \code{\linkS4class{DualSimulations}} object we want to
##' summarize
##' @param trueTox a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity.
##' @param trueBiomarker a function which takes as input a dose (vector) and
##' returns the true biomarker level (vector).
##' @param target the target toxicity interval (default: 20-35%) used for the
##' computations
##' @param \dots Additional arguments can be supplied here for \code{trueTox}
##' and \code{trueBiomarker}
##' @return an object of class \code{\linkS4class{DualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summary-DualSimulations.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="DualSimulations"),
def=
function(object,
trueTox,
trueBiomarker,
target=c(0.2, 0.35),
...){
## call the parent method
start <- callNextMethod(object=object,
truth=trueTox,
target=target,
...)
doseGrid <- object@data[[1]]@doseGrid
## dose level most often selected as MTD
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
## fitted biomarker level at dose most often selected
biomarkerFitAtDoseMostSelected <-
sapply(object@fitBiomarker,
function(f){
f$middleBiomarker[xMostSelected]
})
## mean fitted biomarker curve (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanBiomarkerFitMatrix <- sapply(object@fitBiomarker,
"[[",
"middleBiomarker")
meanBiomarkerFit <- list(truth=
trueBiomarker(doseGrid, ...),
average=
rowMeans(meanBiomarkerFitMatrix),
lower=
apply(meanBiomarkerFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanBiomarkerFitMatrix,
1L,
quantile,
0.975))
## give back an object of class DualSimulationsSummary,
## for which we then define a print / plot method
ret <- .DualSimulationsSummary(
start,
biomarkerFitAtDoseMostSelected=biomarkerFitAtDoseMostSelected,
meanBiomarkerFit=meanBiomarkerFit)
return(ret)
})
##' A Reference Class to represent sequentially updated reporting objects.
##' @name Report
##' @field object The object from which to report
##' @field df the data frame to which columns are sequentially added
##' @field dfNames the names to which strings are sequentially added
Report <-
setRefClass("Report",
fields =
list(object = "ANY",
df = "data.frame",
dfNames = "character"),
methods = list(
dfSave =
function(res, name) {
df <<- cbind(df, res)
dfNames <<- c(dfNames, name)
return(res)
},
report =
function(slotName,
description,
percent=TRUE,
digits=0,
quantiles=c(0.1, 0.9),
subset=NULL,
doSum=FALSE) {
vals <- slot(object, name=slotName)
if(! is.null(subset))
vals <- vals[subset,]
if(doSum)
vals <- apply(vals, 2, sum)
if(percent)
{
unit <- " %"
vals <- vals * 100
} else {
unit <- ""
}
res <- paste(round(mean(vals), digits),
unit,
" (",
paste(round(quantile(vals,
quantiles,
na.rm=TRUE),
digits),
unit,
collapse=", ",
sep=""),
")",
sep="")
## print result to the buffer
cat(description, ":",
"mean",
dfSave(res, slotName),
"\n")
}))
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{GeneralSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="GeneralSimulationsSummary"),
def=
function(object){
r <- Report$new(object=object,
df=
as.data.frame(matrix(nrow=1,
ncol=0)),
dfNames=character())
cat("Summary of",
r$dfSave(object@nsim, "nsim"),
"simulations\n\n")
cat("Target toxicity interval was",
r$dfSave(paste(round(object@target * 100),
collapse=", "),
"targetToxInterval"),
"%\n")
cat("Target dose interval corresponding to this was",
r$dfSave(paste(round(object@targetDoseInterval, 1),
collapse=", "),
"targetDoseInterval"),
"\n")
cat("Intervals are corresponding to",
"10 and 90 % quantiles\n\n")
if(object@placebo){
r$report("nObs",
"Number of patients on placebo",
percent=FALSE,
subset=2)
r$report("nObs",
"Number of patients on active",
percent=FALSE,
subset=1)
r$report("nObs",
"Number of patients overall",
percent=FALSE,
doSum=TRUE)
}else{
r$report("nObs",
"Number of patients overall",
percent=FALSE)
}
r$report("nAboveTarget",
"Number of patients treated above target tox interval",
percent=FALSE)
if(object@placebo){
r$report("propDLTs",
"Proportions of DLTs in the trials for patients on placebo",
subset=2)
r$report("propDLTs",
"Proportions of DLTs in the trials for patients on active",
subset=1)
}else{
r$report("propDLTs",
"Proportions of DLTs in the trials")
}
r$report("meanToxRisk",
"Mean toxicity risks for the patients on active")
r$report("doseSelected",
"Doses selected as MTD",
percent=FALSE, digits=1)
r$report("toxAtDosesSelected",
"True toxicity at doses selected")
cat("Proportion of trials selecting target MTD:",
r$dfSave(object@propAtTarget * 100,
"percentAtTarget"),
"%\n")
cat("Dose most often selected as MTD:",
r$dfSave(object@doseMostSelected,
"doseMostSelected"),
"\n")
cat("Observed toxicity rate at dose most often selected:",
r$dfSave(round(object@obsToxRateAtDoseMostSelected * 100),
"obsToxRateAtDoseMostSelected"),
"%\n")
## finally assign names to the df
## and return it invisibly
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{SimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-show-SimulationsSummary.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="SimulationsSummary"),
def=
function(object){
## call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
## start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
## add one reporting line
r$report("fitAtDoseMostSelected",
"Fitted toxicity rate at dose most often selected")
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Show the summary of the dual-endpoint simulations
##'
##' @param object the \code{\linkS4class{DualSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-show-DualSimulationsSummary.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="DualSimulationsSummary"),
def=
function(object){
## call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
## start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
## add one reporting line
r$report("biomarkerFitAtDoseMostSelected",
"Fitted biomarker level at dose most often selected",
percent=FALSE,
digits=1)
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
##' Graphical display of the general simulation summary
##'
##' This plot method can be applied to
##' \code{\linkS4class{GeneralSimulationsSummary}} objects in order to
##' summarize them graphically. Possible \code{type}s of plots at the moment
##' are:
##'
##' \describe{
##' \item{nObs}{Distribution of the number of patients in the simulated trials}
##' \item{doseSelected}{Distribution of the final selected doses in the trials.
##' Note that this can include zero entries, meaning that the trial was stopped
##' because all doses in the dose grid appeared too toxic.}
##' \item{propDLTs}{Distribution of the proportion of patients with DLTs in the
##' trials}
##' \item{nAboveTarget}{Distribution of the number of patients treated at doses
##' which are above the target toxicity interval (as specified by the
##' \code{truth} and \code{target} arguments to
##' \code{\link{summary,GeneralSimulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{GeneralSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="GeneralSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## distribution of overall sample size
if(x@placebo){
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs[2,],
description="Number of patients on active in total")
}
}else{
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs,
description="Number of patients in total")
}
}
## distribution of final MTD estimate
if("doseSelected" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@doseSelected,
description="MTD estimate")
}
## distribution of proportion of DLTs
if(x@placebo){
if("propDLTs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLTs[1,] * 100,
description="Proportion of DLTs [%] on active",
xaxisround=1)
}
}else{
if("propDLTs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLTs * 100,
description="Proportion of DLTs [%]",
xaxisround=1)
}
}
## distribution of number of patients treated at too much tox
if("nAboveTarget" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nAboveTarget,
description="Number of patients above target")
}
## first combine these small plots
if(length(plotList))
{
ret <-
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just use that
plotList[[1L]]
} else {
## multiple plots in this case
do.call(gridExtra::arrangeGrob,
plotList)
}
}
## then return
ret
})
##' Plot summaries of the model-based design simulations
##'
##' Graphical display of the simulation summary
##'
##' This plot method can be applied to \code{\linkS4class{SimulationsSummary}}
##' objects in order to summarize them graphically. Possible \code{type} of
##' plots at the moment are those listed in
##' \code{\link{plot,GeneralSimulationsSummary,missing-method}} plus:
##' \describe{
##' \item{meanFit}{Plot showing the average fitted dose-toxicity curve across
##' the trials, together with 95% credible intervals, and comparison with the
##' assumed truth (as specified by the \code{truth} argument to
##' \code{\link{summary,Simulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{SimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-SimulationsSummary.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="SimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget",
"meanFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for model-based
## designs
typeReduced <- setdiff(type,
"meanFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanFit plot requested?
if("meanFit" %in% type)
{
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity",
"95% interval for estimated toxicity")
## create the data frame, with
## true tox, average estimated tox, and 95% (lower, upper)
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1,
"95% interval for estimated toxicity"=2)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2,
"95% interval for estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLT [%]")
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot)
else
thisPlot
}
## then finally plot everything
ret
})
##' Plot summaries of the dual-endpoint design simulations
##'
##' This plot method can be applied to \code{\linkS4class{DualSimulationsSummary}}
##' objects in order to summarize them graphically. Possible \code{type} of
##' plots at the moment are those listed in
##' \code{\link{plot,SimulationsSummary,missing-method}} plus:
##' \describe{
##' \item{meanBiomarkerFit}{Plot showing the average fitted dose-biomarker curve across
##' the trials, together with 95% credible intervals, and comparison with the
##' assumed truth (as specified by the \code{trueBiomarker} argument to
##' \code{\link{summary,DualSimulations-method}})}
##' }
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{DualSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plot-DualSimulationsSummary.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="DualSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLTs",
"nAboveTarget",
"meanFit",
"meanBiomarkerFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for dual-endpoint
## designs
typeReduced <- setdiff(type,
"meanBiomarkerFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanBiomarkerFit plot requested?
if("meanBiomarkerFit" %in% type)
{
## which types of lines do we have?
linetype <- c("True biomarker",
"Average estimated biomarker",
"95% interval for estimated biomarker")
## create the data frame, with
## true biomarker, average estimated biomarker, and 95% (lower, upper)
## estimated biomarker stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanBiomarkerFit))
## linetypes for the plot
lt <- c("True biomarker"=1,
"Average estimated biomarker"=1,
"95% interval for estimated biomarker"=2)
## colour for the plot
col <- c("True biomarker"=1,
"Average estimated biomarker"=2,
"95% interval for estimated biomarker"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Biomarker level")
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot, heights=c(2/3, 1/3))
else
thisPlot
}
## then finally plot everything
ret
})
## --------------------------------------------------------------------------------------------------------
##' Summarize the simulations, relative to a given truth
##'
##' @param object the \code{\linkS4class{PseudoSimulations}} object we want to
##' summarize
##' @param truth a function which takes as input a dose (vector) and returns the
##' true probability (vector) for toxicity
##' @param targetEndOfTrial the target probability of DLE wanted to achieve at the end of a trial
##' @param targetDuringTrial the target probability of DLE wanted to achieve during a trial
##'
##' @param \dots Additional arguments can be supplied here for \code{truth}
##' @return an object of class \code{\linkS4class{PseudoSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMsingle.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoSimulations"),
def=
function(object,
truth,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##extract dose grid
doseGrid <- object@data[[1]]@doseGrid
##evaluate true DLE at doseGrid
trueDLE <- truth(doseGrid)
##Inverse function of the truth function
inverse = function (f, lower = -100, upper = 100) {
function (y) uniroot((function (x) f(x) - y), lower = lower, upper = upper)[1]
}
##Function to obtain corresponsing dose level given target prob
TD <- inverse(truth, 0, max(doseGrid))
##Find the dose corresponding to the target dose during trial
targetDoseEndOfTrial <- as.numeric(TD(targetEndOfTrial))
##Find the dose corresponding to the target does end of trial
targetDoseDuringTrial <- as.numeric(TD(targetDuringTrial))
##Find the dose at doseGrid corresponding to the above two quantities
targetDoseEndOfTrialAtDoseGrid <- doseGrid[max(which(targetDoseEndOfTrial-doseGrid >= 0))]
targetDoseDuringTrialAtDoseGrid <- doseGrid[max(which(targetDoseDuringTrial-doseGrid >= 0))]
##A summary for all TDtargetEndOfTrial dose obtained
TDEOTSummary <- summary(object@FinalTDtargetEndOfTrialEstimates)
FinalDoseRecSummary <- TDEOTSummary
ratioTDEOTSummary <- summary(object@FinalTDEOTRatios)
FinalRatioSummary <- ratioTDEOTSummary
##A summary for all TDtargetDuringTrial dose obtained
TDDTSummary <- summary(object@FinalTDtargetDuringTrialEstimates)
##what are the levels above target End of Trial?
xAboveTargetEndOfTrial <- which(trueDLE > targetEndOfTrial)
##what are the levels above target During Trial?
xAboveTargetDuringTrial<- which(trueDLE > targetDuringTrial)
##proportion of DLEs in this trial
propDLE<- sapply(object@data,
function(d) {
mean(d@y)
})
### mean toxicity risk
meanToxRisk <- sapply(object@data,
function(d){
mean(trueDLE[d@xLevel])
})
## doses selected for MTD
doseSelected <- object@doses
## replace NA by 0
doseSelected[is.na(doseSelected)] <- 0
## dose most often selected as MTD
doseMostSelected <-
as.numeric(names(which.max(table(doseSelected))))
#doseRec <- doseMostSelected
xMostSelected <-
matchTolerance(doseMostSelected,
table=doseGrid)
## observed toxicity rate at dose most often selected
## Note: this does not seem very useful!
## Reason: In case of a fine grid, few patients if any
## will have been treated at this dose.
tmp <-
sapply(object@data,
function(d){
whichAtThisDose <- which(d@x == doseMostSelected)
nAtThisDose <- length(whichAtThisDose)
nDLTatThisDose <- sum(d@y[whichAtThisDose])
return(c(nAtThisDose=nAtThisDose,
nDLTatThisDose=nDLTatThisDose))
})
obsToxRateAtDoseMostSelected <-
mean(tmp["nDLTatThisDose",]) / mean(tmp["nAtThisDose",])
## number of patients overall
nObs <- sapply(object@data,
slot,
"nObs")
## number of patients treated above target End of trial
nAboveTargetEndOfTrial <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTargetEndOfTrial)
})
## number of patients treated above target During trial
nAboveTargetDuringTrial <- sapply(object@data,
function(d){
sum(d@xLevel %in% xAboveTargetDuringTrial)
})
toxAtDoses <- truth(doseSelected)
## Proportion of trials selecting target TDEndOfTrial and TDDuringTrial
nsim <- length(object@data)
propAtTargetEndOfTrial <- (length(which(object@doses==targetDoseEndOfTrialAtDoseGrid)))/nsim
propAtTargetDuringTrial <- (length(which(object@doses==targetDoseDuringTrialAtDoseGrid)))/nsim
RecDoseSummary <- TDEOTSummary
##fitted probDLE at dose most often selected
##find names in the fit list (check it is with or without samples)
FitNames<- sapply(object@fit,names)
if ("probDLE" %in% FitNames){
fitAtDoseMostSelected <- sapply(object@fit,
function(f){
f$probDLE[xMostSelected]
})
meanFitMatrix <- sapply(object@fit,
"[[",
"probDLE")
meanFit <- list(truth=
truth(doseGrid),
average=rowMeans(meanFitMatrix))
} else {
## fitted toxicity rate at dose most often selected
fitAtDoseMostSelected <-
sapply(object@fit,
function(f){
f$middle[xMostSelected]
})
## mean fitted toxicity (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanFitMatrix <- sapply(object@fit,
"[[",
"middle")
meanFit <- list(truth=
truth(doseGrid),
average=
rowMeans(meanFitMatrix),
lower=
apply(meanFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanFitMatrix,
1L,
quantile,
0.975))
}
## give back an object of class GeneralSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoSimulationsSummary(
targetEndOfTrial=targetEndOfTrial,
targetDoseEndOfTrial=targetDoseEndOfTrial,
targetDuringTrial=targetDuringTrial,
targetDoseDuringTrial=targetDoseDuringTrial,
targetDoseEndOfTrialAtDoseGrid=targetDoseEndOfTrialAtDoseGrid,
targetDoseDuringTrialAtDoseGrid=targetDoseDuringTrialAtDoseGrid,
TDEOTSummary=TDEOTSummary,
TDDTSummary=TDDTSummary,
FinalDoseRecSummary=FinalDoseRecSummary,
ratioTDEOTSummary=ratioTDEOTSummary,
FinalRatioSummary=FinalRatioSummary,
nsim=length(object@data),
propDLE=propDLE,
meanToxRisk=meanToxRisk,
doseSelected=doseSelected,
doseMostSelected=doseMostSelected,
#doseRec=doseRec,
obsToxRateAtDoseMostSelected=obsToxRateAtDoseMostSelected,
nObs=nObs,
nAboveTargetEndOfTrial=nAboveTargetEndOfTrial,
nAboveTargetDuringTrial=nAboveTargetDuringTrial,
toxAtDosesSelected=toxAtDoses,
propAtTargetEndOfTrial=propAtTargetEndOfTrial,
propAtTargetDuringTrial=propAtTargetDuringTrial,
doseGrid=doseGrid,
fitAtDoseMostSelected=fitAtDoseMostSelected,
meanFit=meanFit)
return(ret)
})
## ========================================================================================================
##' Show the summary of the simulations
##'
##' @param object the \code{\linkS4class{PseudoSimulationsSummary}} object we want
##' to print
##' @return invisibly returns a data frame of the results with one row and
##' appropriate column names
##'
##' @example examples/Simulations-method-showSIMsingle.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="PseudoSimulationsSummary"),
def=
function(object){
r <- Report$new(object=object,
df=
as.data.frame(matrix(nrow=1,
ncol=0)),
dfNames=character())
cat("Summary of",
r$dfSave(object@nsim, "nsim"),
"simulations\n\n")
cat("Target probability of DLE p(DLE) used at the end of a trial was",
r$dfSave(object@targetEndOfTrial * 100,
"targetEndOfTrial"),"%\n")
cat("The dose level corresponds to the target p(DLE) used at the end of a trial, TDEOT, was",
r$dfSave(object@targetDoseEndOfTrial,
"targetDoseEndOfTrial"),"\n")
cat("TDEOT at dose Grid was",
r$dfSave(object@targetDoseEndOfTrialAtDoseGrid,
"targetDoseEndOfTrialAtDoseGrid"),"\n")
cat("Target p(DLE) used during a trial was",
r$dfSave(object@targetDuringTrial * 100,
"targetDuringTrial"),"%\n")
cat("The dose level corresponds to the target p(DLE) used during a trial, TDDT, was",
r$dfSave(object@targetDoseDuringTrial,
"targetDoseDuringTrial"),"\n")
cat("TDDT at dose Grid was",
r$dfSave(object@targetDoseDuringTrialAtDoseGrid,
"targetDoseDuringTrialAtDoseGrid"),"\n")
r$report("nObs",
"Number of patients overall",
percent=FALSE)
r$report("nAboveTargetEndOfTrial",
"Number of patients treated above the target p(DLE) used at the end of a trial",
percent=FALSE)
r$report("nAboveTargetDuringTrial",
"Number of patients treated above the target p(DLE) used during a trial",
percent=FALSE)
r$report("propDLE",
"Proportions of observed DLT in the trials")
r$report("meanToxRisk",
"Mean toxicity risks for the patients")
r$report("doseSelected",
"Doses selected as TDEOT",
percent=FALSE, digits=1)
#r$report("doseRec",
# "Doses to recommend to subsequent study",
# percent=FALSE, digits=1)
r$report("toxAtDosesSelected",
"True toxicity at TDEOT")
cat("Proportion of trials selecting the TDEOT:",
r$dfSave(object@propAtTargetEndOfTrial * 100,
"percentAtTarget"),
"%\n")
cat("Proportion of trials selecting the TDDT:",
r$dfSave(object@propAtTargetDuringTrial * 100,
"percentAtTarget"),
"%\n")
cat("Dose most often selected as TDEOT:",
r$dfSave(object@doseMostSelected,
"doseMostSelected"),
"\n")
cat("Observed toxicity rate at dose most often selected:",
r$dfSave(round(object@obsToxRateAtDoseMostSelected * 100),
"obsToxRateAtDoseMostSelected"),
"%\n")
r$report("fitAtDoseMostSelected",
"Fitted probabilities of DLE at dose most often selected")
TDEOTSum <- object@TDEOTSummary
r$dfSave(as.numeric(TDEOTSum[1]),"TDEOTMin")
r$dfSave(as.numeric(TDEOTSum[2]),"TDEOTlower")
r$dfSave(as.numeric(TDEOTSum[3]),"TDEOTMedian")
r$dfSave(as.numeric(TDEOTSum[4]),"TDEOTMean")
r$dfSave(as.numeric(TDEOTSum[5]),"TDEOTUpper")
r$dfSave(as.numeric(TDEOTSum[6]),"TDEOTMax")
cat("The summary table of the final TDEOT across all simulations\n",
capture.output(TDEOTSum)[1],"\n",
capture.output(TDEOTSum)[2],"\n")
ratioTDEOTSum <-object@ratioTDEOTSummary
r$dfSave(as.numeric(ratioTDEOTSum[1]),"ratioTDEOTMin")
r$dfSave(as.numeric(ratioTDEOTSum[2]),"ratioTDEOTlower")
r$dfSave(as.numeric(ratioTDEOTSum[3]),"ratioTDEOTMedian")
r$dfSave(as.numeric(ratioTDEOTSum[4]),"ratioTDEOTMean")
r$dfSave(as.numeric(ratioTDEOTSum[5]),"ratioTDEOTUpper")
r$dfSave(as.numeric(ratioTDEOTSum[6]),"ratioTDEOTMax")
cat("The summary table of the final ratios of the TDEOT across all simulations\n",
capture.output(ratioTDEOTSum)[1],"\n",
capture.output(ratioTDEOTSum)[2],"\n")
TDDTSum <- object@TDDTSummary
r$dfSave(as.numeric(TDDTSum[1]),"TDDTMin")
r$dfSave(as.numeric(TDDTSum[2]),"TDDTlower")
r$dfSave(as.numeric(TDDTSum[3]),"TDDTMedian")
r$dfSave(as.numeric(TDDTSum[4]),"TDDTMean")
r$dfSave(as.numeric(TDDTSum[5]),"TDDTUpper")
r$dfSave(as.numeric(TDDTSum[6]),"TDDTMax")
cat("The summary table of the final TDDT across all simulations\n",
capture.output(TDDTSum)[1],"\n",
capture.output(TDDTSum)[2],"\n")
FinalDoseRecSum <- object@FinalDoseRecSummary
r$dfSave(as.numeric(FinalDoseRecSum[1]),"FinalDoseRecMin")
r$dfSave(as.numeric(FinalDoseRecSum[2]),"FinalDoseReclower")
r$dfSave(as.numeric(FinalDoseRecSum[3]),"FinalDoseRecMedian")
r$dfSave(as.numeric(FinalDoseRecSum[4]),"FinalDoseRecMean")
r$dfSave(as.numeric(FinalDoseRecSum[5]),"FinalDoseRecUpper")
r$dfSave(as.numeric(FinalDoseRecSum[6]),"FinalDoseRecMax")
cat("The summary table of dose levels, the optimal dose\n to recommend for subsequent study across all simulations\n",
capture.output(FinalDoseRecSum)[1],"\n",
capture.output(FinalDoseRecSum)[2],"\n")
FinalratioSum <-object@FinalRatioSummary
r$dfSave(as.numeric(FinalratioSum[1]),"FinalratioMin")
r$dfSave(as.numeric(FinalratioSum[2]),"Finalratiolower")
r$dfSave(as.numeric(FinalratioSum[3]),"FinalratioMedian")
r$dfSave(as.numeric(FinalratioSum[4]),"FinalratioMean")
r$dfSave(as.numeric(FinalratioSum[5]),"FinalratioUpper")
r$dfSave(as.numeric(FinalratioSum[6]),"FinalratioMax")
cat("The summary table of the final ratios of the optimal dose for stopping across
all simulations\n",
capture.output(FinalratioSum)[1],"\n",
capture.output(FinalratioSum)[2],"\n\n")
## finally assign names to the df
## and return it invisibly
names(r$df) <- r$dfNames
invisible(r$df)
})
## -------------------------------------------------------------------------------------------
##' Plot summaries of the pseudo simulations
##'
##' Graphical display of the simulation summary
##'
##' This plot method can be applied to \code{\linkS4class{PseudoSimulationsSummary}}
##' objects in order to summarize them graphically. This can be used when only DLE responses are involved
##' in the simulations. This also applied to results with or without samples generated during the simulations
##'
##' @param x the \code{\linkS4class{PseudoSimulationsSummary}} object we want
##' to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSUMsingle.R
##' @export
##' @keywords methods
##'
setMethod("plot",
signature=
signature(x="PseudoSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLE",
"nAboveTargetEndOfTrial",
"meanFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## start the plot list
plotList <- list()
plotIndex <- 0L
## distribution of overall sample size
if("nObs" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nObs,
description="Number of patients in total")
}
## distribution of final MTD estimate
if("doseSelected" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@doseSelected,
description="MTD estimate")
}
## distribution of proportion of DLTs
if("propDLE" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@propDLE * 100,
description="Proportion of DLE [%]",
xaxisround=1)
}
## distribution of number of patients treated at too much tox
if("nAboveTargetEndOfTrial" %in% type)
{
plotList[[plotIndex <- plotIndex + 1L]] <-
myBarplot(x=x@nAboveTargetEndOfTrial,
description="Number of patients above target")
}
## first combine these small plots
if(length(plotList))
{
ret <-
## if there is only one plot
if(identical(length(plotList),
1L))
{
## just use that
plotList[[1L]]
} else {
## multiple plots in this case
do.call(gridExtra::arrangeGrob,
plotList)
}
}
##the meanFit plot
if ("meanFit" %in% type)
{## Find if DLE samples are generated in the simulations
## by checking if there the lower limits of the 95% Credibility
## interval are calculated
if (!is.null(x@meanFit$lower)) {
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity",
"95% interval for estimated toxicity")
## create the data frame, with
## true tox, average estimated tox, and 95% (lower, upper)
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1,
"95% interval for estimated toxicity"=2)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2,
"95% interval for estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLE [%]")} else {
## which types of lines do we have?
linetype <- c("True toxicity",
"Average estimated toxicity")
## create the data frame, with
## true tox, average estimated tox
## estimated tox (in percentage) stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 2L),
group=
rep(1:2, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanFit) * 100)
## linetypes for the plot
lt <- c("True toxicity"=1,
"Average estimated toxicity"=1)
## colour for the plot
col <- c("True toxicity"=1,
"Average estimated toxicity"=2)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Probability of DLE [%]")}
}
## then add this plot at the bottom
ret <- gridExtra::arrangeGrob(ret,thisPlot)
ret
})
## --------------------------------------------------------------------------------------
##' Plot simulations
##'
##' Summarize the simulations with plots
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} \item{sigma2}{The variance of the efficacy responses}}
##' You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMDual.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,
"sigma2")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## Efficacy variance estimates boxplot
if("sigma2" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2est), geom="boxplot",
xlab="", ylab="Efficacy variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret,heights=c(2/3, 1/3))
}
return(ret)
})
## ---------------------------------------------------------------------------------
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualFlexiSimulations}}
##' objects in order to summarize them graphically. Possible \code{type}s of
##' plots at the moment are: \describe{ \item{trajectory}{Summary of the
##' trajectory of the simulated trials} \item{dosesTried}{Average proportions of
##' the doses tested in patients} \item{sigma2}{The variance of the efficacy responses}
##' \item{sigma2betaW}{The variance of the random walk model}}
##' You can specify one or both of these in the
##' \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualFlexiSimulations}} object we want
##' to plot from
##' @param y missing
##' @param type the type of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 ggplot geom_step geom_bar aes xlab ylab
##' scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSIMDualFlexi.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualFlexiSimulations",
y="missing"),
def=
function(x,
y,
type=
c("trajectory",
"dosesTried",
"sigma2",
"sigma2betaW"),
...){
## start the plot list
plotList <- list()
plotIndex <- 0L
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for
## dual-endpoint simulation results
typeReduced <- setdiff(type,"sigma2betaW")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
genPlot <- callNextMethod(x=x, y=y, type=typeReduced)
}
## now to the specific dual-endpoint plots:
## random walk model variance estimates boxplot
if("sigma2betaW" %in% type)
{
## save the plot
plotList[[plotIndex <- plotIndex + 1L]] <-
qplot(factor(0), y=y, data=data.frame(y=x@sigma2betaWest), geom="boxplot",
xlab="", ylab="Random walk model variance estimates") +
coord_flip() + scale_x_discrete(breaks=NULL)
}
## then finally plot everything
if(identical(length(plotList),
0L))
{
return(genPlot)
} else if(identical(length(plotList),
1L))
{
ret <- plotList[[1L]]
} else {
ret <- do.call(gridExtra::arrangeGrob,
plotList)
}
if(moreFromGeneral)
{
ret <- gridExtra::arrangeGrob(genPlot, ret,heights=c(2/3, 1/3))
}
return(ret)
})
## -----------------------------------------------------------------------------------------
##' Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model
##' (except the EffFlexi class model)
##'
##' @param object the \code{\linkS4class{PseudoDualSimulations}} object we want to summarize
##' @param trueDLE a function which takes as input a dose (vector) and returns the true probability (vector)
##' of DLE
##' @param trueEff a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).
##' @param targetEndOfTrial the target probability of DLE that are used at the end of a trial. Default at 0.3.
##' @param targetDuringTrial the target probability of DLE that are used during the trial. Default at 0.35.
##' @param \dots Additional arguments can be supplied here for \code{trueDLE} and \code{trueEff}
##' @return an object of class \code{\linkS4class{PseudoDualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMDual.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoDualSimulations"),
def=
function(object,
trueDLE,
trueEff,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##call the parent method
start <- callNextMethod(object=object,
truth=trueDLE,
targetEndOfTrial=targetEndOfTrial,
targetDuringTrial=targetDuringTrial,
...)
doseGrid <- object@data[[1]]@doseGrid
## ## dose level most often selected as MTD (TDtargetEnd of Trial)
xMostSelected <-
matchTolerance(start@doseMostSelected,
table=doseGrid)
##check if true Eff is a function
## check if special case applies
isTrueEffFx <- is.function(trueEff)
TDtargetEndOfTrial <- start@targetDoseEndOfTrial
if (isTrueEffFx) {
negtrueGainfn <- function(dose)
{return(-(trueEff(dose))/(1+(trueDLE(dose)/(1-trueDLE(dose)))))}
Gstar <- optim(exp(1),negtrueGainfn,method="BFGS")$par
maxGainValue <- -(optim(exp(1),negtrueGainfn,method="BFGS")$value)
GstarAtDoseGrid <- doseGrid[max(which(Gstar-doseGrid >= 0 ))]
} else {
trueGain <- (trueEff)/(1+(trueDLE(doseGrid)/(1-trueDLE(doseGrid))))
maxGainValue<-max(trueGain)
Gstar <- doseGrid[which.max(trueGain)]
GstarAtDoseGrid <- Gstar
}
##A summary for all final Gstar obtained
GstarSummary <- summary(object@FinalGstarEstimates)
ratioGstarSummary <- summary(object@FinalGstarRatios)
FinalDoseRecSummary <- summary(object@FinalOptimalDose)
FinalRatioSummary <- summary(object@FinalRatios)
##find names in the fit efficacy list (check it is with or without samples)
FitNames<- sapply(object@fitEff,names)
if ("ExpEff" %in% FitNames){
## fitted efficacy level at dose most often selected
EffFitAtDoseMostSelected <- sapply(object@fitEff,
function(f){
f$ExpEff[xMostSelected]
})
meanEffFitMatrix <- sapply(object@fitEff,
"[[",
"ExpEff")
meanEffFit <- list(truth=
trueEff(doseGrid),
average=rowMeans(meanEffFitMatrix))
} else {## fitted efficacy level at dose most often selected
EffFitAtDoseMostSelected <-
sapply(object@fitEff,
function(f){
f$middle[xMostSelected]
})
## mean fitted curve (average, lower and upper quantiles)
## at each dose level
## (this is required for plotting)
meanEffFitMatrix <- sapply(object@fitEff,
"[[",
"middle")
## check if special case applies
if (isTrueEffFx) {TRUTHeff<- trueEff(doseGrid)} else {TRUTHeff <- trueEff}
meanEffFit <- list(truth=
TRUTHeff,
average=
rowMeans(meanEffFitMatrix),
lower=
apply(meanEffFitMatrix,
1L,
quantile,
0.025),
upper=
apply(meanEffFitMatrix,
1L,
quantile,
0.975))}
## give back an object of class PseudoDualSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoDualSimulationsSummary(
start,
targetGstar=Gstar,
targetGstarAtDoseGrid=GstarAtDoseGrid,
GstarSummary=GstarSummary,
ratioGstarSummary=ratioGstarSummary,
FinalDoseRecSummary=FinalDoseRecSummary,
FinalRatioSummary=FinalRatioSummary,
EffFitAtDoseMostSelected=EffFitAtDoseMostSelected,
meanEffFit=meanEffFit)
return(ret)
})
## --------------------------------------------------------------------------------------------------
##' Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.
##'
##' @param object the \code{\linkS4class{PseudoDualFlexiSimulations}} object we want to summarize
##' @param trueDLE a function which takes as input a dose (vector) and returns the true probability of DLE (vector)
##' @param trueEff a vector which takes as input the true mean efficacy values at all dose levels (in order)
##' @param targetEndOfTrial the target probability of DLE that are used at the end of a trial. Default at 0.3.
##' @param targetDuringTrial the target probability of DLE that are used during the trial. Default at 0.35.
##' @param \dots Additional arguments can be supplied here for \code{trueDLE} and \code{trueEff}
##' @return an object of class \code{\linkS4class{PseudoDualSimulationsSummary}}
##'
##' @example examples/Simulations-method-summarySIMDualFlexi.R
##' @export
##' @keywords methods
setMethod("summary",
signature=
signature(object="PseudoDualFlexiSimulations"),
def=
function(object,
trueDLE,
trueEff,
targetEndOfTrial=0.3,
targetDuringTrial=0.35,
...){
##call the parent method
start <- callNextMethod(object=object,
trueDLE=trueDLE,
trueEff=trueEff,
targetEndOfTrial=targetEndOfTrial,
targetDuringTrial=targetDuringTrial,
...)
## give back an object of class PseudoDualSimulationsSummary,
## for which we then define a print / plot method
ret <- .PseudoDualSimulationsSummary(start)
return(ret)
})
## ----------------------------------------------------------------------------------------
##' Show the summary of Pseudo Dual simulations summary
##'
##' @param object the \code{\linkS4class{PseudoDualSimulationsSummary}} object we want to print
##' @return invisibly returns a data frame of the results with one row and appropriate column names
##'
##'
##' @example examples/Simulations-method-showSIMDual.R
##' @export
##' @keywords methods
setMethod("show",
signature=
signature(object="PseudoDualSimulationsSummary"),
def=
function(object){
##call the parent method
df <- callNextMethod(object)
dfNames <- names(df)
##start report object
r <- Report$new(object=object,
df=df,
dfNames=dfNames)
##add three reporting lines
cat("Target Gstar, the dose which gives the maximum gain value was",
r$dfSave(object@targetGstar,
"targetGstar"),"\n")
cat("Target Gstar at dose Grid was",
r$dfSave(object@targetGstarAtDoseGrid,
"targetGstarAtDoseGrid"),"\n")
GstarSum <- object@GstarSummary
r$dfSave(as.numeric(GstarSum[1]),"GstarMin")
r$dfSave(as.numeric(GstarSum[2]),"Gstarlower")
r$dfSave(as.numeric(GstarSum[3]),"GstarMedian")
r$dfSave(as.numeric(GstarSum[4]),"GstarMean")
r$dfSave(as.numeric(GstarSum[5]),"GstarUpper")
r$dfSave(as.numeric(GstarSum[6]),"GstarMax")
cat("The summary table of the final Gstar across all simulations\n",
capture.output(GstarSum)[1],"\n",
capture.output(GstarSum)[2],"\n")
ratioGstarSum <-object@ratioGstarSummary
r$dfSave(as.numeric(ratioGstarSum[1]),"ratioGstarMin")
r$dfSave(as.numeric(ratioGstarSum[2]),"ratioGstarlower")
r$dfSave(as.numeric(ratioGstarSum[3]),"ratioGstarMedian")
r$dfSave(as.numeric(ratioGstarSum[4]),"ratioGstarMean")
r$dfSave(as.numeric(ratioGstarSum[5]),"ratioGstarUpper")
r$dfSave(as.numeric(ratioGstarSum[6]),"ratioGstarMax")
cat("The summary table of the final ratios of the Gstar across all simulations\n",
capture.output(ratioGstarSum)[1],"\n",
capture.output(ratioGstarSum)[2],"\n")
FinalDoseRecSum <- object@FinalDoseRecSummary
r$dfSave(as.numeric(FinalDoseRecSum[1]),"FinalDoseRecMin")
r$dfSave(as.numeric(FinalDoseRecSum[2]),"FinalDoseReclower")
r$dfSave(as.numeric(FinalDoseRecSum[3]),"FinalDoseRecMedian")
r$dfSave(as.numeric(FinalDoseRecSum[4]),"FinalDoseRecMean")
r$dfSave(as.numeric(FinalDoseRecSum[5]),"FinalDoseRecUpper")
r$dfSave(as.numeric(FinalDoseRecSum[6]),"FinalDoseRecMax")
cat("The summary table of dose levels, the optimal dose\n to recommend for subsequent study across all simulations\n",
capture.output(FinalDoseRecSum)[1],"\n",
capture.output(FinalDoseRecSum)[2],"\n")
FinalratioSum <-object@FinalRatioSummary
r$dfSave(as.numeric(FinalratioSum[1]),"FinalratioMin")
r$dfSave(as.numeric(FinalratioSum[2]),"Finalratiolower")
r$dfSave(as.numeric(FinalratioSum[3]),"FinalratioMedian")
r$dfSave(as.numeric(FinalratioSum[4]),"FinalratioMean")
r$dfSave(as.numeric(FinalratioSum[5]),"FinalratioUpper")
r$dfSave(as.numeric(FinalratioSum[6]),"FinalratioMax")
cat("The summary table of the final ratios of the optimal dose for stopping across
all simulations\n",
capture.output(FinalratioSum)[1],"\n",
capture.output(FinalratioSum)[2],"\n")
r$report("EffFitAtDoseMostSelected",
"Fitted expected efficacy level at dose most often selected",
percent=FALSE,
digits=1)
## and return the updated information
names(r$df) <- r$dfNames
invisible(r$df)
})
## --------------------------------------------------------------------------------------------------
##' Plot the summary of Pseudo Dual Simulations summary
##'
##' This plot method can be applied to \code{\linkS4class{PseudoDualSimulationsSummary}} objects in order
##' to summarize them graphically. Possible \code{type} of plots at the moment are those listed in
##' \code{\link{plot,PseudoSimulationsSummary,missing-method}} plus:
##' \describe{\item{meanEffFit}{Plot showing the fitted dose-efficacy curve. If no samples are involved, only the
##' average fitted dose-efficacy curve across the trials will be plotted. If samples (DLE and efficacy) are involved,
##' the average fitted dose-efficacy curve across the trials, together with the 95% credibility interval; and comparison
##' with the assumed truth (as specified by the \code{trueEff} argument to
##' \code{\link{summary,PseudoDualSimulations-method}})}}
##' You can specify any subset of these in the \code{type} argument.
##'
##' @param x the \code{\linkS4class{PseudoDualSimulationsSummary}} object we want to plot from
##' @param y missing
##' @param type the types of plots you want to obtain.
##' @param \dots not used
##' @return A single \code{\link[ggplot2]{ggplot}} object if a single plot is
##' asked for, otherwise a \code{\link{gridExtra}{gTree}} object.
##'
##' @importFrom ggplot2 geom_histogram ggplot aes xlab ylab geom_line
##' scale_linetype_manual scale_colour_manual
##' @importFrom gridExtra arrangeGrob
##'
##' @example examples/Simulations-method-plotSUMDual.R
##' @export
##' @keywords methods
setMethod("plot",
signature=
signature(x="PseudoDualSimulationsSummary",
y="missing"),
def=
function(x,
y,
type=
c("nObs",
"doseSelected",
"propDLE",
"nAboveTargetEndOfTrial",
"meanFit",
"meanEffFit"),
...){
## which plots should be produced?
type <- match.arg(type,
several.ok=TRUE)
stopifnot(length(type) > 0L)
## substract the specific plot types for dual-endpoint
## designs
typeReduced <- setdiff(type,
"meanEffFit")
## are there more plots from general?
moreFromGeneral <- (length(typeReduced) > 0)
## if so, then produce these plots
if(moreFromGeneral)
{
ret <- callNextMethod(x=x, y=y, type=typeReduced)
}
## is the meanBiomarkerFit plot requested?
if("meanEffFit" %in% type)
{ ## Find if Effsamples are generated in the simulations
## by checking if there the lower limits of the 95% Credibility
## interval are calculated
if (!is.null(x@meanEffFit$lower)) {
## which types of lines do we have?
linetype <- c("True Expected Efficacy",
"Average estimated expected efficacy",
"95% interval for estimated expected efficacy")
## create the data frame, with
## true biomarker, average estimated expected efficacy, and 95% (lower, upper)
## estimated biomarker stacked below each other
dat <- data.frame(dose=
rep(x@doseGrid, 4L),
group=
rep(1:4, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2, 3, 3)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanEffFit))
## linetypes for the plot
lt <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=1,
"95% interval for estimated expected efficacy"=2)
## colour for the plot
col <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=4,
"95% interval for estimated expected efficacy"=4)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Expected Efficacy level")} else {
linetype <- c("True Expected Efficacy",
"Average estimated expected efficacy")
## create the data frame, with
## true biomarker, average estimated expected efficacy
dat <- data.frame(dose=
rep(x@doseGrid, 2L),
group=
rep(1:2, each=length(x@doseGrid)),
linetype=
factor(rep(linetype[c(1, 2)],
each=length(x@doseGrid)),
levels=linetype),
lines=
unlist(x@meanEffFit))
## linetypes for the plot
lt <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=1)
## colour for the plot
col <- c("True Expected Efficacy"=1,
"Average estimated expected efficacy"=4)
## now create and save the plot
thisPlot <- ggplot() +
geom_line(aes(x=dose,
y=lines,
group=group,
linetype=linetype,
col=linetype),
data=dat)
thisPlot <- thisPlot +
scale_linetype_manual(values=lt) +
scale_colour_manual(values=col) +
xlab("Dose level") +
ylab("Expected Efficacy level")
}
## add this plot to the bottom
ret <-
if(moreFromGeneral)
gridExtra::arrangeGrob(ret, thisPlot, heights=c(2/3, 1/3))
else
thisPlot
}
## then finally plot everything
ret
})
## ------------------------------------------------------------------------
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