Nothing
R/backboinetr.R
In bfboinet: Backfill Bayesian Optimal Interval Design Using Efficacy and Toxicity
#' @title backboinetr
#'
#' @description Obtain the operating characteristics of the backfill bayesian
#' optimal interval design using efficacy and toxicity outcomes for dose
#' optimization within random scenarios
#'
#' @param target_T Target toxicity probability. The default value is
#' \code{target_T=0.3}. When observing 1 DLT out of 3 patients and the target
#' DLT rate is between 0.25 and 0.279, the decision is to stay at the current
#' dose due to a widely accepted practice.
#' @param target_Tr The upper boundary for the toxicity when generating the
#' random scenarios. The default value is \code{target_Tr=0.359}.
#' @param target_E The minimum required efficacy probability. The default value
#' is \code{target_E=0.25}.
#' @param target_Er The lower boundary for the efficacy when generating the
#' random scenarios. The default value is \code{target_Er=0.197}.
#' @param n.dose Number of dose.
#' @param startdose Starting dose. The lowest dose is generally recommended.
#' @param ncohort Number of cohort.
#' @param cohortsize Cohort size.
#' @param pT.saf Highest toxicity probability that is deemed sub-therapeutic
#' such that dose-escalation should be pursued. The default value is
#' \code{pT.saf=target_T*0.6}.
#' @param pT.tox Lowest toxicity probability that is deemed overly toxic such
#' that dose de-escalation is needed. The default value is
#' \code{pT.tox=target_T*1.4}.
#' @param pE.saf Minimum probability deemed efficacious such that the dose
#' levels with less than delta1 are considered sub-therapeutic.
#' The default value is \code{pE.saf=target_E*0.6}.
#' @param alpha.T1 Probability that toxicity event occurs in the late half of
#' toxicity assessment window. The default value is \code{alpha.T1=0.5}.
#' @param alpha.E1 Probability that efficacy event occurs in the late half of
#' assessment window. The default value is \code{alpha.E1=0.5}.
#' @param tau.T Toxicity assessment windows (months).
#' @param tau.E Efficacy assessment windows (months).
#' @param te.corr Correlation between toxicity and efficacy probability,
#' specified as Gaussian copula parameter. The default value is
#' \code{te.corr=0.2}.
#' @param gen.event.time Method to generate the time to first toxicity and
#' efficacy outcome. Weibull distribution is used when
#' \code{gen.event.time ="weibull"}. Uniform distribution is used when
#' \code{gen.event.time="uniform"}.
#' The default value is \code{gen.event.time="weibull"}.
#' @param accrual Accrual rate (months) (patient accrual rate per month).
#' @param gen.enroll.time Method to generate enrollment time. Uniform
#' distribution is used when
#' \code{gen.enroll.time="uniform"}. Exponential distribution is used when
#' \code{gen.enroll.time="exponential"}. The default
#' value is \code{gen.enroll.time="uniform"}.
#' @param n.elimination a minimum sample size for dose elimination. If the number
#' of patients treated at the current dose reaches \code{n.elimination} and meet
#' elimination dose level criteria, eliminate current dose level and higher doses
#' when meet toxicity criteria and eliminate current dose level when meet efficacy
#' criteria. The default value is \code{n.elimination=6}.
#' @param stopping.npts Early study termination criteria for the number of
#' patients in the dose-escalation and backfill cohorts. If the number of
#' patients at the current dose reaches this criteria and the same dose level
#' is recommended as the next dose level, the study is terminated.
#' The default value is \code{stopping.npts=12}.
#' @param suspend the suspension rule that holds off the decision on dose
#' allocation for the dose-escalation cohort until sufficient toxicity
#' information is available. For example, setting as 0.33 which means one-third
#' of the patients had not completed the toxicity evaluation at the current dose
#' level in the dose escalation cohort. The default value \code{suspend=0}
#' essentially turns off this type of suspending rule, that is all patients
#' should complete the toxicity evaluation at the current dose level in the dose
#' escalation cohort
#' @param stopping.prob.T Early study termination criteria for toxicity,
#' taking a value between 0 and 1. If the posterior probability that toxicity
#' outcome is less than the target toxicity probability (\code{target_T}) is
#' larger than this criteria, the dose levels are eliminated from the study.
#' The default value is \code{stopping.prob.T=0.95}.
#' @param stopping.prob.E Early study termination criteria for efficacy,
#' taking a value between 0 and 1. If the posterior probability that efficacy
#' outcome is less than the minimum efficacy probability (\code{target_E}) is
#' larger than this criteria, the dose levels are eliminated from the study.
#' The default value is \code{stopping.prob.E=0.90}.
#' @param ppsi01 Score for toxicity=yes and efficacy=no in utility defined by
#' scoring.The default value is \code{psi01=0}.
#' @param ppsi00 Score for toxicity=no and efficacy=no in utility defined by
#' scoring. The default value is \code{psi00=40}.
#' @param ppsi11 Score for toxicity=yes and efficacy=yes in utility defined by
#' scoring. The default value is \code{psi11=60}.
#' @param ppsi10 Score for toxicity=no and efficacy=yes in utility defined by
#' scoring. The default value is \code{psi10=100}.
#' @param n.sim Number of simulated trial. The default value is
#' \code{n.sim=10000}.
#' @param seed.sim Seed for random number generator. The default value is
#' \code{seed.sim=30}.
#' @details The \code{backboinetr} is a function which generates the operating
#' characteristics of the backfill bayesian optimal interval design using
#' efficacy and toxicity outcomes for dose optimization by a simulation study.
#' Users can specify a variety of study settings to simulate studies. The
#' operating characteristics of the design are summarized by the percentage of
#' times that each dose level was selected as optimal biological dose and the
#' average number of patients who were treated at each dose level. The
#' percentage of times that the study was terminated and the expected study
#' duration are also provided.
#' @return
#' The \code{backboinetr} returns a list containing the following components:
#' \item{toxprob}{The random true toxicity probability.}
#' \item{effprob}{The random true efficacy probability.}
#' \item{phi}{Target toxicity probability.}
#' \item{delta}{Target efficacy probability.}
#' \item{target_Tr}{The upper boundary for the toxicity when generating the
#' random scenarios.}
#' \item{target_Er}{The lower boundary for the efficacy when generating the
#' random scenarios.}
#' \item{bd.true}{The target optimal dose (OD) level when generating the random
#' scenarios.}
#' \item{mtd.true}{The maximum tolerated dose (MTD) level when generating the
#' random scenarios.}
#' \item{lambda1}{Lower toxicity boundary in dose escalation/de-escalation.}
#' \item{lambda2}{Upper toxicity boundary in dose escalation/de-escalation.}
#' \item{eta1}{Lower efficacy boundary in dose escalation/de-escalation.}
#' \item{tau.T}{Toxicity assessment windows (months).}
#' \item{tau.E}{Efficacy assessment windows (months).}
#' \item{suspend}{The suspension rule that holds off the decision on dose
#' allocation for the dose-escalation cohort until sufficient toxicity
#' information is available.}
#' \item{accrual}{Accrual rate (months) (patient accrual rate per month).}
#' \item{n.patient}{Average number of patients who were treated at each dose
#' level in dose-esclation and backfill cohorts}
#' \item{n.bpatient}{Average number of back filled patients who were treated
#' at each dose level}
#' \item{n.tox.patient}{Average number of patients who experienced toxicity at
#' each dose level in dose-esclation and backfill cohorts}
#' \item{n.eff.patient}{Average number of patients who experienced efficacy at
#' each dose level in dose-esclation and backfill cohorts}
#' \item{n.tox.bpatient}{Average number of patients who experienced toxicity at
#' each dose level in backfill cohort}
#' \item{n.eff.bpatient}{Average number of patients who experienced efficacy at
#' each dose level in backfill cohort}
#' \item{prop.select}{Percentage of times that each dose level was selected as
#' optimal biological dose.}
#' \item{prop.stop}{Percentage of times that the study was terminated.}
#' \item{duration}{Expected study duration (months)}
#' \item{totaln}{Total patients}
#' \item{data.obs.n}{Record the number of patients in each dose level within the
#' simulations during the trial}
#' \item{obd}{Record the optimal dose in each simulation during the trial}
#' \item{backfilltimes}{Record how may times we back-filled during the trial}
#' \item{backfillcount}{Record the number of back-filled patients in dose level
#' within the simulations during the trial}
#' \item{PCS}{The percentage of trials that the optimal dose was correctly
#' selected.}
#' \item{PCA}{The percentage of patients that were correctly allocated to the
#' optimal dose.}
#' \item{PTS}{The percentage of toxic doses selection.}
#' \item{PTA}{The percentage of patients who were allocated to toxic doses.}
#' @references
#' BF-BOIN-ET: A backfill Bayesian optimal interval design using
#' efficacy and toxicity outcomes for dose optimization.
#' @examples
#'
#' target_T=0.3
#' target_E=0.25
#'
#' get.oc.backboinetr(target_T=target_T,target_Tr=0.359,target_E=target_E,
#' target_Er=0.197,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
#' pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
#' alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
#' gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.elimination=6,
#' stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
#' ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=30)
#'
#' @import Iso copula dplyr tidyselect magrittr
#' @importFrom stats binomial dbinom pbeta pbinom rmultinom runif rexp rbeta
#' @export
get.oc.backboinetr <- function (target_T=0.3,target_Tr=0.359,target_E=0.25,target_Er=0.197,n.dose,startdose,ncohort,cohortsize,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T,tau.E,te.corr=0.2,gen.event.time="weibull",
accrual,gen.enroll.time="uniform",n.elimination=6,stopping.npts=12,
suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,ppsi01=0,ppsi00=40,ppsi11=60,
ppsi10=100,n.sim=10000,seed.sim=30){
if(!((pT.saf<target_T)&(target_T<pT.tox))){
stop("Design parameters must satisfy a condition of pT.saf < target_T < pT.tox.")
}else if(!(pE.saf<target_E)){
stop("Design parameters must satisfy a condition of pE.saf < target_E.")
}
#####Get utility score by measuring the efficacy-toxicity trade off####;
futility_score <- function(pe,pt,psi00,psi01,psi10,psi11)
{
psi.e0t1 <- psi01
psi.e0t0 <- psi00
psi.e1t1 <- psi11
psi.e1t0 <- psi10
ut = ( psi.e0t1*(1-pe)*pt
+ psi.e0t0*(1-pe)*(1-pt)
+ psi.e1t1*pe *pt
+ psi.e1t0*pe *(1-pt))
return(ut)
}
#####Get optimal dose by measuring the efficacy-toxicity trade off####;
obd.select <- function(probt, probe,tterm, eterm, ph,stopT, stopE,psi00, psi01,psi10,psi11)
{
candose <- which((tterm>=(1-stopT))&(eterm>=(1-stopE)))
if( (length(probt[candose])>0) & (length(candose)>0) ){
mdif <- min(abs(probt[candose]-ph))
mtd1 <- max(which(abs(probt-ph)==mdif))
effdose <- intersect(candose,1:mtd1)
}else{
effdose <- 0
}
fu <- futility_score(pt=probt,pe=probe,
psi00=psi00,psi01=psi01,psi10=psi10,psi11=psi11)
if(length(fu[effdose])>0){
fu.max <- which(fu==max(fu[effdose]))
if(length(intersect(fu.max,effdose))>0){
re <- min(intersect(fu.max,effdose))
}else{
re <- 0
}
}else{
re <-0
}
return(re)
}
####calculate toxicity escalation and de-escalation boundaries#####;
lambda1 = log((1 - pT.saf)/(1 - target_T))/log(target_T *
(1 - pT.saf)/(pT.saf * (1 - target_T)))
lambda2 = log((1 - target_T)/(1 - pT.tox))/log(pT.tox * (1 -
target_T)/(target_T * (1 - pT.tox)))
####calculate efficacy boundaries#####;
eta1 = log((1-pE.saf)/(1-target_E))/log(target_E*(1-pE.saf)/(pE.saf* (1-target_E)))
######generate random scenarios#####;
ndose<-n.dose # number of doses
targetT<-target_T # highest acceptable toxicity rate
targetE<-target_Er # lowest acceptable efficacy rate
u1<-ppsi11
u2<-ppsi00
ntrial<-n.sim
obd.vec<-mtd.vec<-OBD.vec<-c()
pE.mat<-pT.mat<-u.mat<-c()
obdi<-0
set.seed(seed.sim)
for (iii in startdose:ndose){
obdi <- iii
for(s in 1:ntrial){
obd<-obdi
mtd<-obd # initialization
prob_plateau<-0.5
obd.temp<-0
jj<-kk<-rep(0,ndose)
uu<-runif(1)
if(uu<prob_plateau & obd<ndose){
# generate plateau cases
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
bornesup<-runif(1,targetE+0.1,1)
jj[1:obd]<-sort(runif(obd,0,bornesup))
jj[obd:ndose]<-jj[obd]
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
if(uu>=prob_plateau & obd<ndose){
# generate non-plateau cases
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd ){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
med<-(obd-1)+sample(ndose-obd+1,1)
bornesup<-runif(1,targetE+0.1,1)
jj[med]<-max(runif(ndose,0,bornesup))
if(med>1){
jj[1:(med-1)]<-sort(runif(med-1,0,jj[med]))
}
if(med<ndose){
jj[(med+1):ndose]<- sort(runif(ndose-med,0,jj[med]),decreasing=TRUE)
}
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
if(obd==ndose){
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
bornesup<-runif(1,targetE+0.1,1)
jj<-sort(runif(ndose,0,bornesup))
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
pE.mat<-rbind(pE.mat,jj)
pT.mat<-rbind(pT.mat,kk)
u.mat<-rbind(u.mat,utility)
obd.vec<-c(obd.vec,obd)
mtd.vec<-c(mtd.vec,mtd)
}
OBD.vec<-c(OBD.vec,rep(iii,ntrial))
all<-list(OBD=OBD.vec,obd.true=obd.vec,mtd.true=mtd.vec,Pe=pE.mat,Pt=pT.mat)
alld<-as.data.frame(all)
}
eff<-alld %>% select(starts_with("Pe."))
tox<-alld %>% select(starts_with("Pt."))
####end#####;
an.sim<-n.sim*(n.dose-startdose+1)
data.obs.n <- array(0,dim=c(an.sim,n.dose))
toxprobr <- array(0,dim=c(an.sim,n.dose))
effprobr <- array(0,dim=c(an.sim,n.dose))
data.dur <- array(0,dim=c(an.sim))
obd <- array(0,dim=c(an.sim))
mtd.true <- array(0,dim=c(an.sim))
bd <- array(0,dim=c(an.sim))
toxicity=matrix(nrow=an.sim,ncol=n.dose)
efficacy=matrix(nrow=an.sim,ncol=n.dose)
btoxicity=matrix(nrow=an.sim,ncol=n.dose)
befficacy=matrix(nrow=an.sim,ncol=n.dose)
prop.select <- array(0,dim=c(an.sim,n.dose))
prop.stop <- array(0,dim=c(an.sim))
dose.curr=startdose
backfilltimes=rep(0,an.sim) ## record how may times we back-filled during the trial
backfillcount=matrix(nrow=an.sim,ncol=n.dose) ## record the location of backfill
nmax=ncohort*cohortsize ####max number of patients for dose-escalation#####;
dosen <- 1:n.dose
dose <- paste("Dose",dosen,sep="")
pr.alpha <- 1
pr.beta <- 1
alpha.T1 <- alpha.T1
alpha.T2 <- 0.5
alpha.E1 <- alpha.E1
alpha.E2 <- 0.5
for(simu in 1:an.sim){
set.seed(seed.sim)
effprob = eff[simu,]
toxprob = tox[simu,]
bd[simu]=alld[simu,2]
mtd.true[simu]=alld[simu,3]
toxp <- data.frame(toxprob)
colnames(toxp) <- dose
effp <- data.frame(effprob)
colnames(effp) <- dose
efftoxp <- list(toxp=toxp,effp=effp)
ncop <- copula::normalCopula(te.corr,dim=2,dispstr="ex")
mv.ncop <- NULL
if(gen.event.time=="weibull"){
for(i in 1:n.dose){
psi.T <- efftoxp$toxp[i][[1]]
zetta.T1 <- log(log(1-psi.T)/log(1-psi.T+alpha.T1*psi.T))/log(1/(1-alpha.T2))
zetta.T2 <- tau.T/(-log(1-psi.T))^(1/zetta.T1)
psi.E <- efftoxp$effp[i][[1]]
zetta.E1 <- log(log(1-psi.E)/log(1-psi.E+alpha.E1*psi.E))/log(1/(1-alpha.E2))
zetta.E2 <- tau.E/(-log(1-psi.E))^(1/zetta.E1)
mv.ncop <- append(mv.ncop,copula::mvdc(copula = ncop,
margins = c("weibull","weibull"),
paramMargins = list(list(shape=zetta.T1,scale=zetta.T2),
list(shape=zetta.E1,scale=zetta.E2))))
}
}else if(gen.event.time=="uniform"){
for(i in 1:n.dose){
psi.T <- efftoxp$toxp[i][[1]]
psi.E <- efftoxp$effp[i][[1]]
mv.ncop <- append(mv.ncop,copula::mvdc(copula = ncop,
margins = c("unif","unif"),
paramMargins = list(list(min=0,max=tau.T*(1/psi.T)),
list(min=0,max=tau.E*(1/psi.E)))))
}
}
#####end of generating event time for efficacy and toxicity######;
yE=NULL
yT=NULL
localt.yT=NULL
localt.yE=NULL
obs.n <- numeric(n.dose)
obs.tox <- numeric(n.dose)
obs.tox.n <- numeric(n.dose)
obs.eff <- numeric(n.dose)
obs.eff.n <- numeric(n.dose)
pe <- numeric(n.dose)
pt <- numeric(n.dose)
obs.btox <- numeric(n.dose)
obs.beff <- numeric(n.dose)
dose.curr <- startdose
bgamma <- numeric(n.dose)
d=NULL ####record dose allocation####
t.enter=NULL
localt.enter=NULL
localt.finish=NULL
t.eventT=NULL
t.eventE=NULL
t.onset=NULL
t.finish=NULL
t.curr=0
tite.df <- NULL
nvector=rep(0,n.dose)
bdosekeep=NULL
backfill=0 # how many times of backfill.
backfillvector=rep(0,length=n.dose) ## which dose is backfilled.
elimi_tox<-rep(0,n.dose)
elimi_eff<-rep(0,n.dose)
elimi<-rep(0,n.dose)
earlystop<-0
type=NULL
for(i in 1:ncohort){
### assign three maxpatients to dose.curr
# t.curr; t.finish;t.onset
localt.enter=NULL
for(j in 1:cohortsize){
## local t.enter
if(j==1){
localt.enter=c(localt.enter,t.curr)
}else{
if(gen.enroll.time=="uniform"){ localt.enter =c(localt.enter, localt.enter[length(localt.enter)]+ runif(1, 0, 2/accrual))}
if(gen.enroll.time=="exponential"){ localt.enter = c(localt.enter, localt.enter[length(localt.enter)]+ rexp(1, rate=accrual))}
}
}
localt.finish=localt.enter+tau.T
t.enter=c(t.enter,localt.enter)
t.finish=c(t.finish,localt.finish)
time.te <- copula::rMvdc(cohortsize,mv.ncop[[dose.curr]]) ####event time#####
localt.onset=localt.enter+time.te[,1]
localt.yT=as.numeric(time.te[,1]<=tau.T)
localt.yE=as.numeric(time.te[,2]<=tau.E)
yT=c(yT,as.numeric(time.te[,1]<=tau.T))
yE=c(yE,as.numeric(time.te[,2]<=tau.E))
d=c(d,rep(dose.curr,cohortsize))
type=c(type,rep(1,cohortsize))
t.eventT=c(t.eventT,time.te[,1])
t.eventE=c(t.eventE,time.te[,2])
t.onset=t.enter+apply(data.frame(
t.eventT=t.eventT,
t.eventE=t.eventE
), 1, max, na.rm=TRUE)
endt=apply(data.frame(
endt1=t.enter+tau.T,
endt2=t.enter+t.eventT
), 1, min, na.rm=TRUE)
#####added for more than or equal 1-suspend patients completed the DLT assessment####;
if (suspend!=0){
quantile=quantile(endt[which(d==dose.curr)], probs = (1-suspend))
quantile50=max(endt[which(d==dose.curr)][endt[which(d==dose.curr)]<=quantile])
}
#####end######;
ende=apply(data.frame(
ende1=t.enter+tau.E,
ende2=t.enter+t.eventE
), 1, min, na.rm=TRUE)
nvector[dose.curr]=nvector[dose.curr]+cohortsize
## renew t.curr until there is open doses
flag=0
t.curr=max(t.enter)
if (suspend!=0){
t.bench=min(max(localt.finish),max(localt.onset),quantile50)
}else{
t.bench=min(max(localt.finish),max(localt.onset))
}
if(i==ncohort){t.bench=t.bench+100}
###bacause in the last cohort, we need to enroll enough patients to the backfilling doses, setting as 100 months#######;
queue=NULL
# t.enter;t.onset;t.finish
while(flag==0){
flag=1
if(gen.enroll.time=="uniform"){
t.curr=t.curr+runif(1, 0, 2/accrual)
queue=c(queue,t.curr)
}else if(gen.enroll.time=="exponential"){
t.curr=t.curr+rexp(1, rate=accrual)
queue=c(queue,t.curr)
}
if(t.bench>t.curr){
flag=0
}
}
queue=queue[which(queue!=t.curr)]
db=NULL ##record dose allocation for backfilling
localt.enterb=NULL
localt.finishb=NULL
localt.yTb=NULL
localt.yEb=NULL
time.teba=NULL
titeb.df=NULL
s_back=NULL
# t.curr;queue
# d
## check open doses
## be adviced, we do not eliminate a dose from bf set if it turns of be overtoxic during the trial.
## assume response is immediately available after dose assignment.
## this simplifies the situation, during backfilling, no lower doses watch response
## if not, change the code easily, decide the bfset everytime we queueing
flag2=0
while(flag2==0){
flag2=1
if(dose.curr!=1){ # dynnamically change the bfset as each person arrives.
for (q in queue){
####select max utility score for backfilling dose####;
if(!is.null(bdosekeep)){
if (q!=min(queue)){
#####using the latest information to get the max utility score#####
titeb.df <- data.frame(dose = d,
enter = t.enter,
endtox = endt,
dlt = yT,
endeff = ende,
orr = yE)
for(ds in 1:n.dose){
if(sum(titeb.df$dose==ds)>0){
bcompsub.T <- titeb.df[(titeb.df$endtox<=q)&(titeb.df$dose==ds),]
bpendsub.T <- titeb.df[(titeb.df$endtox >q)&(titeb.df$dose==ds),]
bcompsub.E <- titeb.df[(titeb.df$endeff<=q)&(titeb.df$dose==ds),]
bpendsub.E <- titeb.df[(titeb.df$endeff >q)&(titeb.df$dose==ds),]
bx.DLT <- sum(bcompsub.T$dlt)
bn.DLT <- bx.DLT+sum(1-bcompsub.T$dlt)+sum(q-bpendsub.T$enter)/tau.T
bx.ORR <- sum(bcompsub.E$orr)
bn.ORR <- bx.ORR+sum(1-bcompsub.E$orr)+sum(q-bpendsub.E$enter)/tau.E
obs.tox[ds] <- bx.DLT
obs.tox.n[ds] <- bn.DLT
pt[ds] <- bx.DLT/bn.DLT
obs.eff[ds] <- bx.ORR
obs.eff.n[ds] <- bn.ORR
pe[ds] <- bx.ORR/bn.ORR
titeb.curdose <- titeb.df[titeb.df$dose==ds,]
bgamma.T <- as.numeric(titeb.curdose$endtox<=q)
bgamma.E <- as.numeric(titeb.curdose$endeff<=q)
bgamma[ds]<- mean(bgamma.T*bgamma.E)
}}
}
###select the backfilling dose using utility score#####;
####add utility score#####;
evadose <- intersect(intersect(dosen[nvector!=0],bdosekeep),dosen[nvector<stopping.npts])
if(!identical(evadose, integer(0))) {
estpt <- Iso::pava(pt[evadose])
utility_b<-futility_score(pe=pe[evadose],pt=estpt,psi00=ppsi00,psi01=ppsi01,psi10=ppsi10,psi11=ppsi11)
s_back=max(evadose[utility_b==max(utility_b)]) ###backfilling dose#####;
}else{
s_back=NULL
}
}
## check the status of current dos, if it all finishes and reaches n.stop and decision is stay, terminate the trial
if(!is.null(s_back)){
s=s_back
if(nvector[s]<stopping.npts){
## note, I do not care if toxicity of this backfill dose is currently over toxic or not.
db=c(db,s)
d=c(d,s)
type=c(type,0)
localt.enterb=c(localt.enterb,q)
localt.finishb=c(localt.finishb,q+tau.T)
t.enter=c(t.enter,q)
t.finish=c(t.finish, q+tau.T)
time.teb<-copula::rMvdc(1,mv.ncop[[s]])
time.teba<-rbind(time.teba,time.teb)
localt.yTb=c(localt.yTb,as.numeric(time.teb[,1]<=tau.T))
localt.yEb=c(localt.yEb,as.numeric(time.teb[,2]<=tau.E))
yT=c(yT,as.numeric(time.teb[,1]<=tau.T))
yE=c(yE,as.numeric(time.teb[,2]<=tau.E))
t.eventT=c(t.eventT,time.teb[,1])
t.eventE=c(t.eventE,time.teb[,2])
t.onset=t.enter+apply(data.frame(
t.eventT=t.eventT,
t.eventE=t.eventE
), 1, max, na.rm=TRUE)
endt=apply(data.frame(
endt1=t.enter+tau.T,
endt2=t.enter+t.eventT
), 1, min, na.rm=TRUE)
ende=apply(data.frame(
endeb1=t.enter+tau.E,
endeb2=t.enter+t.eventE
), 1, min, na.rm=TRUE)
nvector[s]=nvector[s]+1
backfillvector[s]=backfillvector[s]+1
backfill=backfill+1
}
}
} # end of queue
# queue;t.enter;t.event;t.onset;t.finish;d;yT;yE;nvector; backfillvector; d
} # dose.curr !=1
}
####decide next dose#####;
tite.df <- rbind(tite.df,
data.frame(dose = dose.curr,
enter = localt.enter,
endtox = localt.enter+apply(as.matrix(1:cohortsize),1,function(x){min(time.te[x,1],tau.T)}),
dlt = localt.yT,
endeff = localt.enter+apply(as.matrix(1:cohortsize),1,function(x){min(time.te[x,2],tau.E)}),
orr = localt.yE,
backfill = rep(0,cohortsize)))
if(!is.null(localt.enterb)){
tite.df <- rbind(tite.df,
data.frame(dose = db,
enter = localt.enterb,
endtox = localt.enterb+apply(as.matrix(1:length(localt.enterb)),1,function(x){min(time.teba[x,1],tau.T)}),
dlt = localt.yTb,
endeff = localt.enterb+apply(as.matrix(1:length(localt.enterb)),1,function(x){min(time.teba[x,2],tau.E)}),
orr = localt.yEb,
backfill = rep(1,length(localt.enterb))))
}
tite.curdose <- tite.df[tite.df$dose==dose.curr,]
gamma.T <- as.numeric(tite.curdose$endtox<=t.curr)
gamma.E <- as.numeric(tite.curdose$endeff<=t.curr)
gamma.all.T <- as.numeric(tite.df$endtox<=t.curr)
gamma.all.E <- as.numeric(tite.df$endeff<=t.curr)
for(ds in 1:n.dose){
if(sum(tite.df$dose==ds)>0){
compsub.T <- tite.df[(tite.df$endtox<=t.curr)&(tite.df$dose==ds),]
pendsub.T <- tite.df[(tite.df$endtox >t.curr)&(tite.df$dose==ds),]
compsub.E <- tite.df[(tite.df$endeff<=t.curr)&(tite.df$dose==ds),]
pendsub.E <- tite.df[(tite.df$endeff >t.curr)&(tite.df$dose==ds),]
x.DLT <- sum(compsub.T$dlt)
n.DLT <- x.DLT+sum(1-compsub.T$dlt)+sum(t.curr-pendsub.T$enter)/tau.T
x.ORR <- sum(compsub.E$orr)
n.ORR <- x.ORR+sum(1-compsub.E$orr)+sum(t.curr-pendsub.E$enter)/tau.E
obs.tox[ds] <- x.DLT
obs.tox.n[ds] <- n.DLT
pt[ds] <- x.DLT/n.DLT
obs.eff[ds] <- x.ORR
obs.eff.n[ds] <- n.ORR
pe[ds] <- x.ORR/n.ORR
#####create two paramters for - Average number of patients who experienced toxicity/efficacy at each dose level in backfill cohort####;
x.bDLT <- sum(compsub.T[compsub.T$backfill==1,]$dlt)
x.bORR <- sum(compsub.E[compsub.E$backfill==1,]$orr)
obs.btox[ds] <- x.bDLT
obs.beff[ds] <- x.bORR
}}
#####dose allocation#####;
if(pt[dose.curr]<=lambda1){
nxtdose <- dose.curr+1
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr){
backdose<-c(jmin:dose.curr)
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}else if( ((pt[dose.curr]>lambda1) & (pt[dose.curr]<=lambda2)) | ( (obs.tox[dose.curr]==1) & (nvector[dose.curr]==3)) ){
nxtdose <- dose.curr
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr-1){
backdose<-c(jmin:(dose.curr-1))
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}else if(pt[dose.curr]>lambda2){
nxtdose <- dose.curr-1
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr-2){
backdose<-c(jmin:(dose.curr-2))
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}
####eliminate dose#####;
po.shape1 <- pr.alpha + obs.tox
po.shape2 <- pr.beta + (nvector-obs.tox)
tterm <- pbeta(target_T,po.shape1,po.shape2)
po.shape1 <- pr.alpha + obs.eff
po.shape2 <- pr.beta + (nvector-obs.eff)
eterm <- 1-pbeta(target_E,po.shape1,po.shape2)
###determine which dose level should be eliminated
if(length(which((tterm<(1-stopping.prob.T))&(nvector>=n.elimination)))>0){
elimi_tox[(min(which((tterm<(1-stopping.prob.T))&(nvector>=n.elimination)))):n.dose]<-1
}
if(length(which((eterm<(1-stopping.prob.E))&(nvector>=n.elimination)))>0){
elimi_eff[which((eterm<(1-stopping.prob.E))&(nvector>=n.elimination))]<-1
}
elimi<-elimi_tox+elimi_eff
admflg <- (elimi==0)
admdose <- dosen[admflg]
if(sum(admflg)==0){
earlystop=1
break
}else if((length(d[which(type==1)]))==nmax){ ## dose-escalation patients are consumed! ##enrollment is stopped when 1)
break
}else if((nvector[dose.curr]>=stopping.npts) & ( ##enrollment is stopped when 2)
((pt[dose.curr]>lambda1) & (pt[dose.curr]<=lambda2)) | (dose.curr==1 & pt[dose.curr]>=lambda2) | ((dose.curr==n.dose||admflg[dose.curr+1]==0) & pt[dose.curr]<=lambda1)
)){
break
}else{
if(nxtdose==0){
if(admflg[1]){
dose.curr <- 1
}else{
break
}
}else if(nxtdose==(n.dose+1)){
if(admflg[n.dose]){
dose.curr <- n.dose
}else{
break
}
}else if(is.element(nxtdose,admdose)){
dose.curr <- nxtdose
}else if(dose.curr<nxtdose){
if(sum(admdose>=nxtdose)!=0){
dose.curr <- min(admdose[admdose>=nxtdose])
}
}else if(dose.curr>=nxtdose){
if(sum(admdose<=nxtdose)!=0){
dose.curr <- max(admdose[admdose<=nxtdose])
}else{
break
}
}
####for backfilling doses#####;
if(!is.null(backdose)){
if(length(intersect(backdose,admdose))>0){
bdosekeep<-intersect(backdose,admdose)
}else{
bdosekeep<-NULL
}
}else{
bdosekeep<-NULL
}
####end;
}
}
t.curr=max(endt,ende)
data.obs.n[simu,] <- nvector
data.dur[simu] <- t.curr
toxprobr[simu,] <- as.vector(t(toxprob))
effprobr[simu,] <- as.vector(t(effprob))
efficacy[simu,]=obs.eff
toxicity[simu,]=obs.tox
befficacy[simu,]=obs.beff
btoxicity[simu,]=obs.btox
backfilltimes[simu]=backfill
backfillcount[simu,]=backfillvector
evadose <- intersect(dosen[nvector!=0],dosen[])
obspt <- obs.tox[evadose]/nvector[evadose]
obspe <- obs.eff[evadose]/nvector[evadose]
tterm.obd <- numeric(n.dose)
eterm.obd <- numeric(n.dose)
for(i in evadose){
po.shape1 <- pr.alpha + obs.tox[i]
po.shape2 <- pr.beta + (nvector[i]-obs.tox[i])
tterm.obd[i] <- pbeta(target_T,po.shape1,po.shape2)
po.shape1 <- pr.alpha + obs.eff[i]
po.shape2 <- pr.beta + (nvector[i]-obs.eff[i])
eterm.obd[i] <- 1-pbeta(target_E,po.shape1,po.shape2)
}
if(length(d)<nmax & earlystop==1){
obd[simu] <- 0
}else if(length(evadose)==1){
if((tterm.obd[evadose]>=(1-stopping.prob.T))&(eterm.obd[evadose]>=(1-stopping.prob.E))){
obd[simu] <- evadose
}
}else if(sum((tterm.obd[evadose]>=(1-stopping.prob.T))&(eterm.obd[evadose]>=(1-stopping.prob.E)))>=1){
estpt <- Iso::pava(obspt)
estpe <- obspe
obd[simu] <- obd.select(probt=estpt, probe=estpe,tterm=tterm.obd[evadose], eterm=eterm.obd[evadose],
stopT=stopping.prob.T, stopE=stopping.prob.E , ph=target_T ,psi00=ppsi00, psi01=ppsi01,psi10=ppsi10,psi11=ppsi11)
}
####output results####;
for(i in 1:n.dose){
prop.select[simu,i] <- round(mean(obd[simu]==i)*100,digits=1)
}
colnames(prop.select) <- dose
prop.stop[simu] <- round(mean(obd[simu]==0)*100,digits=1)
}
n.patient <- round(apply(data.obs.n,2,mean),digits=2)
names(n.patient) <- dose
n.bpatient <- round(apply(backfillcount,2,mean),digits=2)
names(n.bpatient) <- dose
n.tox.patient <- round(apply(toxicity,2,mean),digits=2)
names(n.tox.patient) <- dose
n.eff.patient <- round(apply(efficacy,2,mean),digits=2)
names(n.eff.patient) <- dose
n.tox.bpatient <- round(apply(btoxicity,2,mean),digits=2)
names(n.tox.bpatient) <- dose
n.eff.bpatient <- round(apply(befficacy,2,mean),digits=2)
names(n.eff.bpatient) <- dose
names(toxprob) <- dose
names(effprob) <- dose
duration <- round(mean(data.dur),digits=1)
names(duration) <- "Trial duration"
totaln = sum(data.obs.n)/an.sim
names(totaln)<-"Total patients"
names(target_T) <- "Target toxicity prob."
names(target_E) <- "Target efficacy prob."
names(lambda1) <- "Lower toxicity boundary"
names(lambda2) <- "Upper toxicity boundary"
names(eta1) <- "Lower efficacy boundary"
names(tau.T) <- "Tox. assessment window (months)"
names(tau.E) <- "Eff. assessment window (months)"
names(accrual) <- "Accrual rate (months)"
names(suspend) <- "Suspension rule"
names(target_Tr) <- "The upper boundary for the toxicity"
names(target_Er) <- "The lower boundary for the efficacy"
colnames(toxprobr) <- dose
colnames(effprobr) <- dose
colnames(data.obs.n) <- dose
colnames(backfillcount) <- dose
PCSA <- array(0,dim=c(an.sim))
PCAA <- array(0,dim=c(an.sim))
POAA <- array(0,dim=c(an.sim))
POS=sum(obd>mtd.true)*100/an.sim
for (k in 1:an.sim){
if(bd[k]==0){
PCSA[k]=prop.stop[k]
PCAA[k]=0
}else{
PCSA[k]=prop.select[k,bd[k]]
PCAA[k]=(data.obs.n[k,]*100/sum(data.obs.n[k,]))[bd[k]]
}
if (mtd.true[k]==n.dose){POAA[k]=0}else{
POAA[k]= sum(data.obs.n[k,(mtd.true[k]+1):n.dose])*100/sum(data.obs.n[k,])# percent of overdose allocation
}
}
POA=round(mean(POAA),digits=1)
PCS=round(mean(PCSA),digits=1)
PCA=round(mean(PCAA),digits=1)
names(PCS) <-"The percentage of trials that the optimal dose was correctly selected"
names(PCA) <-"The percentage of patients that were correctly allocated to the optimal dose"
names(POS) <-"The percentage of toxic doses selection"
names(POA) <-"The percentage of patients who were allocated to toxic doses"
result <- list(toxprob = toxprobr,
effprob = effprobr,
phi = target_T,
delta = target_E,
target_Tr = target_Tr,
target_Er = target_Er,
bd.true = bd,
mtd.true = mtd.true,
lambda1 = lambda1,
lambda2 = lambda2,
eta1 = eta1,
tau.T = tau.T,
tau.E = tau.E,
suspend = suspend,
accrual = accrual,
n.patient = n.patient,
n.bpatient = n.bpatient,
n.tox.patient= n.tox.patient,
n.eff.patient= n.eff.patient,
n.tox.bpatient= n.tox.bpatient,
n.eff.bpatient= n.eff.bpatient,
prop.select = prop.select,
prop.stop = prop.stop,
duration = duration,
totaln = totaln,
data.obs.n = data.obs.n,
obd = obd,
backfilltimes= backfilltimes,
backfillcount= backfillcount,
PCS=PCS,
PCA=PCA,
PTS=POS,
PTA=POA
)
class(result) <- "BF-BOIN-ET-R"
return(result)
}
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#' @title backboinetr
#'
#' @description Obtain the operating characteristics of the backfill bayesian
#' optimal interval design using efficacy and toxicity outcomes for dose
#' optimization within random scenarios
#'
#' @param target_T Target toxicity probability. The default value is
#' \code{target_T=0.3}. When observing 1 DLT out of 3 patients and the target
#' DLT rate is between 0.25 and 0.279, the decision is to stay at the current
#' dose due to a widely accepted practice.
#' @param target_Tr The upper boundary for the toxicity when generating the
#' random scenarios. The default value is \code{target_Tr=0.359}.
#' @param target_E The minimum required efficacy probability. The default value
#' is \code{target_E=0.25}.
#' @param target_Er The lower boundary for the efficacy when generating the
#' random scenarios. The default value is \code{target_Er=0.197}.
#' @param n.dose Number of dose.
#' @param startdose Starting dose. The lowest dose is generally recommended.
#' @param ncohort Number of cohort.
#' @param cohortsize Cohort size.
#' @param pT.saf Highest toxicity probability that is deemed sub-therapeutic
#' such that dose-escalation should be pursued. The default value is
#' \code{pT.saf=target_T*0.6}.
#' @param pT.tox Lowest toxicity probability that is deemed overly toxic such
#' that dose de-escalation is needed. The default value is
#' \code{pT.tox=target_T*1.4}.
#' @param pE.saf Minimum probability deemed efficacious such that the dose
#' levels with less than delta1 are considered sub-therapeutic.
#' The default value is \code{pE.saf=target_E*0.6}.
#' @param alpha.T1 Probability that toxicity event occurs in the late half of
#' toxicity assessment window. The default value is \code{alpha.T1=0.5}.
#' @param alpha.E1 Probability that efficacy event occurs in the late half of
#' assessment window. The default value is \code{alpha.E1=0.5}.
#' @param tau.T Toxicity assessment windows (months).
#' @param tau.E Efficacy assessment windows (months).
#' @param te.corr Correlation between toxicity and efficacy probability,
#' specified as Gaussian copula parameter. The default value is
#' \code{te.corr=0.2}.
#' @param gen.event.time Method to generate the time to first toxicity and
#' efficacy outcome. Weibull distribution is used when
#' \code{gen.event.time ="weibull"}. Uniform distribution is used when
#' \code{gen.event.time="uniform"}.
#' The default value is \code{gen.event.time="weibull"}.
#' @param accrual Accrual rate (months) (patient accrual rate per month).
#' @param gen.enroll.time Method to generate enrollment time. Uniform
#' distribution is used when
#' \code{gen.enroll.time="uniform"}. Exponential distribution is used when
#' \code{gen.enroll.time="exponential"}. The default
#' value is \code{gen.enroll.time="uniform"}.
#' @param n.elimination a minimum sample size for dose elimination. If the number
#' of patients treated at the current dose reaches \code{n.elimination} and meet
#' elimination dose level criteria, eliminate current dose level and higher doses
#' when meet toxicity criteria and eliminate current dose level when meet efficacy
#' criteria. The default value is \code{n.elimination=6}.
#' @param stopping.npts Early study termination criteria for the number of
#' patients in the dose-escalation and backfill cohorts. If the number of
#' patients at the current dose reaches this criteria and the same dose level
#' is recommended as the next dose level, the study is terminated.
#' The default value is \code{stopping.npts=12}.
#' @param suspend the suspension rule that holds off the decision on dose
#' allocation for the dose-escalation cohort until sufficient toxicity
#' information is available. For example, setting as 0.33 which means one-third
#' of the patients had not completed the toxicity evaluation at the current dose
#' level in the dose escalation cohort. The default value \code{suspend=0}
#' essentially turns off this type of suspending rule, that is all patients
#' should complete the toxicity evaluation at the current dose level in the dose
#' escalation cohort
#' @param stopping.prob.T Early study termination criteria for toxicity,
#' taking a value between 0 and 1. If the posterior probability that toxicity
#' outcome is less than the target toxicity probability (\code{target_T}) is
#' larger than this criteria, the dose levels are eliminated from the study.
#' The default value is \code{stopping.prob.T=0.95}.
#' @param stopping.prob.E Early study termination criteria for efficacy,
#' taking a value between 0 and 1. If the posterior probability that efficacy
#' outcome is less than the minimum efficacy probability (\code{target_E}) is
#' larger than this criteria, the dose levels are eliminated from the study.
#' The default value is \code{stopping.prob.E=0.90}.
#' @param ppsi01 Score for toxicity=yes and efficacy=no in utility defined by
#' scoring.The default value is \code{psi01=0}.
#' @param ppsi00 Score for toxicity=no and efficacy=no in utility defined by
#' scoring. The default value is \code{psi00=40}.
#' @param ppsi11 Score for toxicity=yes and efficacy=yes in utility defined by
#' scoring. The default value is \code{psi11=60}.
#' @param ppsi10 Score for toxicity=no and efficacy=yes in utility defined by
#' scoring. The default value is \code{psi10=100}.
#' @param n.sim Number of simulated trial. The default value is
#' \code{n.sim=10000}.
#' @param seed.sim Seed for random number generator. The default value is
#' \code{seed.sim=30}.
#' @details The \code{backboinetr} is a function which generates the operating
#' characteristics of the backfill bayesian optimal interval design using
#' efficacy and toxicity outcomes for dose optimization by a simulation study.
#' Users can specify a variety of study settings to simulate studies. The
#' operating characteristics of the design are summarized by the percentage of
#' times that each dose level was selected as optimal biological dose and the
#' average number of patients who were treated at each dose level. The
#' percentage of times that the study was terminated and the expected study
#' duration are also provided.
#' @return
#' The \code{backboinetr} returns a list containing the following components:
#' \item{toxprob}{The random true toxicity probability.}
#' \item{effprob}{The random true efficacy probability.}
#' \item{phi}{Target toxicity probability.}
#' \item{delta}{Target efficacy probability.}
#' \item{target_Tr}{The upper boundary for the toxicity when generating the
#' random scenarios.}
#' \item{target_Er}{The lower boundary for the efficacy when generating the
#' random scenarios.}
#' \item{bd.true}{The target optimal dose (OD) level when generating the random
#' scenarios.}
#' \item{mtd.true}{The maximum tolerated dose (MTD) level when generating the
#' random scenarios.}
#' \item{lambda1}{Lower toxicity boundary in dose escalation/de-escalation.}
#' \item{lambda2}{Upper toxicity boundary in dose escalation/de-escalation.}
#' \item{eta1}{Lower efficacy boundary in dose escalation/de-escalation.}
#' \item{tau.T}{Toxicity assessment windows (months).}
#' \item{tau.E}{Efficacy assessment windows (months).}
#' \item{suspend}{The suspension rule that holds off the decision on dose
#' allocation for the dose-escalation cohort until sufficient toxicity
#' information is available.}
#' \item{accrual}{Accrual rate (months) (patient accrual rate per month).}
#' \item{n.patient}{Average number of patients who were treated at each dose
#' level in dose-esclation and backfill cohorts}
#' \item{n.bpatient}{Average number of back filled patients who were treated
#' at each dose level}
#' \item{n.tox.patient}{Average number of patients who experienced toxicity at
#' each dose level in dose-esclation and backfill cohorts}
#' \item{n.eff.patient}{Average number of patients who experienced efficacy at
#' each dose level in dose-esclation and backfill cohorts}
#' \item{n.tox.bpatient}{Average number of patients who experienced toxicity at
#' each dose level in backfill cohort}
#' \item{n.eff.bpatient}{Average number of patients who experienced efficacy at
#' each dose level in backfill cohort}
#' \item{prop.select}{Percentage of times that each dose level was selected as
#' optimal biological dose.}
#' \item{prop.stop}{Percentage of times that the study was terminated.}
#' \item{duration}{Expected study duration (months)}
#' \item{totaln}{Total patients}
#' \item{data.obs.n}{Record the number of patients in each dose level within the
#' simulations during the trial}
#' \item{obd}{Record the optimal dose in each simulation during the trial}
#' \item{backfilltimes}{Record how may times we back-filled during the trial}
#' \item{backfillcount}{Record the number of back-filled patients in dose level
#' within the simulations during the trial}
#' \item{PCS}{The percentage of trials that the optimal dose was correctly
#' selected.}
#' \item{PCA}{The percentage of patients that were correctly allocated to the
#' optimal dose.}
#' \item{PTS}{The percentage of toxic doses selection.}
#' \item{PTA}{The percentage of patients who were allocated to toxic doses.}
#' @references
#' BF-BOIN-ET: A backfill Bayesian optimal interval design using
#' efficacy and toxicity outcomes for dose optimization.
#' @examples
#'
#' target_T=0.3
#' target_E=0.25
#'
#' get.oc.backboinetr(target_T=target_T,target_Tr=0.359,target_E=target_E,
#' target_Er=0.197,n.dose=5,startdose=1,ncohort=10,cohortsize=3,
#' pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
#' alpha.T1=0.5,alpha.E1=0.5,tau.T=1,tau.E=1,te.corr=0.2,
#' gen.event.time="weibull",accrual=3,gen.enroll.time="uniform",n.elimination=6,
#' stopping.npts=12,suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,
#' ppsi01=0,ppsi00=40,ppsi11=60,ppsi10=100,n.sim=2,seed.sim=30)
#'
#' @import Iso copula dplyr tidyselect magrittr
#' @importFrom stats binomial dbinom pbeta pbinom rmultinom runif rexp rbeta
#' @export
get.oc.backboinetr <- function (target_T=0.3,target_Tr=0.359,target_E=0.25,target_Er=0.197,n.dose,startdose,ncohort,cohortsize,
pT.saf=0.6 * target_T,pT.tox = 1.4 * target_T,pE.saf = 0.6 * target_E,
alpha.T1=0.5,alpha.E1=0.5,tau.T,tau.E,te.corr=0.2,gen.event.time="weibull",
accrual,gen.enroll.time="uniform",n.elimination=6,stopping.npts=12,
suspend=0,stopping.prob.T=0.95,stopping.prob.E=0.90,ppsi01=0,ppsi00=40,ppsi11=60,
ppsi10=100,n.sim=10000,seed.sim=30){
if(!((pT.saf<target_T)&(target_T<pT.tox))){
stop("Design parameters must satisfy a condition of pT.saf < target_T < pT.tox.")
}else if(!(pE.saf<target_E)){
stop("Design parameters must satisfy a condition of pE.saf < target_E.")
}
#####Get utility score by measuring the efficacy-toxicity trade off####;
futility_score <- function(pe,pt,psi00,psi01,psi10,psi11)
{
psi.e0t1 <- psi01
psi.e0t0 <- psi00
psi.e1t1 <- psi11
psi.e1t0 <- psi10
ut = ( psi.e0t1*(1-pe)*pt
+ psi.e0t0*(1-pe)*(1-pt)
+ psi.e1t1*pe *pt
+ psi.e1t0*pe *(1-pt))
return(ut)
}
#####Get optimal dose by measuring the efficacy-toxicity trade off####;
obd.select <- function(probt, probe,tterm, eterm, ph,stopT, stopE,psi00, psi01,psi10,psi11)
{
candose <- which((tterm>=(1-stopT))&(eterm>=(1-stopE)))
if( (length(probt[candose])>0) & (length(candose)>0) ){
mdif <- min(abs(probt[candose]-ph))
mtd1 <- max(which(abs(probt-ph)==mdif))
effdose <- intersect(candose,1:mtd1)
}else{
effdose <- 0
}
fu <- futility_score(pt=probt,pe=probe,
psi00=psi00,psi01=psi01,psi10=psi10,psi11=psi11)
if(length(fu[effdose])>0){
fu.max <- which(fu==max(fu[effdose]))
if(length(intersect(fu.max,effdose))>0){
re <- min(intersect(fu.max,effdose))
}else{
re <- 0
}
}else{
re <-0
}
return(re)
}
####calculate toxicity escalation and de-escalation boundaries#####;
lambda1 = log((1 - pT.saf)/(1 - target_T))/log(target_T *
(1 - pT.saf)/(pT.saf * (1 - target_T)))
lambda2 = log((1 - target_T)/(1 - pT.tox))/log(pT.tox * (1 -
target_T)/(target_T * (1 - pT.tox)))
####calculate efficacy boundaries#####;
eta1 = log((1-pE.saf)/(1-target_E))/log(target_E*(1-pE.saf)/(pE.saf* (1-target_E)))
######generate random scenarios#####;
ndose<-n.dose # number of doses
targetT<-target_T # highest acceptable toxicity rate
targetE<-target_Er # lowest acceptable efficacy rate
u1<-ppsi11
u2<-ppsi00
ntrial<-n.sim
obd.vec<-mtd.vec<-OBD.vec<-c()
pE.mat<-pT.mat<-u.mat<-c()
obdi<-0
set.seed(seed.sim)
for (iii in startdose:ndose){
obdi <- iii
for(s in 1:ntrial){
obd<-obdi
mtd<-obd # initialization
prob_plateau<-0.5
obd.temp<-0
jj<-kk<-rep(0,ndose)
uu<-runif(1)
if(uu<prob_plateau & obd<ndose){
# generate plateau cases
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
bornesup<-runif(1,targetE+0.1,1)
jj[1:obd]<-sort(runif(obd,0,bornesup))
jj[obd:ndose]<-jj[obd]
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
if(uu>=prob_plateau & obd<ndose){
# generate non-plateau cases
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd ){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
med<-(obd-1)+sample(ndose-obd+1,1)
bornesup<-runif(1,targetE+0.1,1)
jj[med]<-max(runif(ndose,0,bornesup))
if(med>1){
jj[1:(med-1)]<-sort(runif(med-1,0,jj[med]))
}
if(med<ndose){
jj[(med+1):ndose]<- sort(runif(ndose-med,0,jj[med]),decreasing=TRUE)
}
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
if(obd==ndose){
while(obd.temp!=obd | kk[obd]>target_Tr | jj[obd]<targetE+0.05){
mtd<-(obd-1)+sample(ndose-obd+1,1)
D=1:ndose
if(mtd<ndose){temp<-sort(runif(length(D)-mtd,targetT,1));
bornesup<-max(targetT+0.1,temp[length(D)-mtd])}
if(mtd==ndose){bornesup<-targetT +(1-targetT)*rbeta(1,0.5,1)}
mtd.temp<-0
while(mtd.temp!=mtd){
kk<-sort(runif(ndose,0,bornesup))
mtd.temp<-which.min(abs(kk-targetT))
}
bornesup<-runif(1,targetE+0.1,1)
jj<-sort(runif(ndose,0,bornesup))
utility<-u1*jj+(1-kk)*u2
obd.temp<-which.max(utility[1:mtd])
}
}
pE.mat<-rbind(pE.mat,jj)
pT.mat<-rbind(pT.mat,kk)
u.mat<-rbind(u.mat,utility)
obd.vec<-c(obd.vec,obd)
mtd.vec<-c(mtd.vec,mtd)
}
OBD.vec<-c(OBD.vec,rep(iii,ntrial))
all<-list(OBD=OBD.vec,obd.true=obd.vec,mtd.true=mtd.vec,Pe=pE.mat,Pt=pT.mat)
alld<-as.data.frame(all)
}
eff<-alld %>% select(starts_with("Pe."))
tox<-alld %>% select(starts_with("Pt."))
####end#####;
an.sim<-n.sim*(n.dose-startdose+1)
data.obs.n <- array(0,dim=c(an.sim,n.dose))
toxprobr <- array(0,dim=c(an.sim,n.dose))
effprobr <- array(0,dim=c(an.sim,n.dose))
data.dur <- array(0,dim=c(an.sim))
obd <- array(0,dim=c(an.sim))
mtd.true <- array(0,dim=c(an.sim))
bd <- array(0,dim=c(an.sim))
toxicity=matrix(nrow=an.sim,ncol=n.dose)
efficacy=matrix(nrow=an.sim,ncol=n.dose)
btoxicity=matrix(nrow=an.sim,ncol=n.dose)
befficacy=matrix(nrow=an.sim,ncol=n.dose)
prop.select <- array(0,dim=c(an.sim,n.dose))
prop.stop <- array(0,dim=c(an.sim))
dose.curr=startdose
backfilltimes=rep(0,an.sim) ## record how may times we back-filled during the trial
backfillcount=matrix(nrow=an.sim,ncol=n.dose) ## record the location of backfill
nmax=ncohort*cohortsize ####max number of patients for dose-escalation#####;
dosen <- 1:n.dose
dose <- paste("Dose",dosen,sep="")
pr.alpha <- 1
pr.beta <- 1
alpha.T1 <- alpha.T1
alpha.T2 <- 0.5
alpha.E1 <- alpha.E1
alpha.E2 <- 0.5
for(simu in 1:an.sim){
set.seed(seed.sim)
effprob = eff[simu,]
toxprob = tox[simu,]
bd[simu]=alld[simu,2]
mtd.true[simu]=alld[simu,3]
toxp <- data.frame(toxprob)
colnames(toxp) <- dose
effp <- data.frame(effprob)
colnames(effp) <- dose
efftoxp <- list(toxp=toxp,effp=effp)
ncop <- copula::normalCopula(te.corr,dim=2,dispstr="ex")
mv.ncop <- NULL
if(gen.event.time=="weibull"){
for(i in 1:n.dose){
psi.T <- efftoxp$toxp[i][[1]]
zetta.T1 <- log(log(1-psi.T)/log(1-psi.T+alpha.T1*psi.T))/log(1/(1-alpha.T2))
zetta.T2 <- tau.T/(-log(1-psi.T))^(1/zetta.T1)
psi.E <- efftoxp$effp[i][[1]]
zetta.E1 <- log(log(1-psi.E)/log(1-psi.E+alpha.E1*psi.E))/log(1/(1-alpha.E2))
zetta.E2 <- tau.E/(-log(1-psi.E))^(1/zetta.E1)
mv.ncop <- append(mv.ncop,copula::mvdc(copula = ncop,
margins = c("weibull","weibull"),
paramMargins = list(list(shape=zetta.T1,scale=zetta.T2),
list(shape=zetta.E1,scale=zetta.E2))))
}
}else if(gen.event.time=="uniform"){
for(i in 1:n.dose){
psi.T <- efftoxp$toxp[i][[1]]
psi.E <- efftoxp$effp[i][[1]]
mv.ncop <- append(mv.ncop,copula::mvdc(copula = ncop,
margins = c("unif","unif"),
paramMargins = list(list(min=0,max=tau.T*(1/psi.T)),
list(min=0,max=tau.E*(1/psi.E)))))
}
}
#####end of generating event time for efficacy and toxicity######;
yE=NULL
yT=NULL
localt.yT=NULL
localt.yE=NULL
obs.n <- numeric(n.dose)
obs.tox <- numeric(n.dose)
obs.tox.n <- numeric(n.dose)
obs.eff <- numeric(n.dose)
obs.eff.n <- numeric(n.dose)
pe <- numeric(n.dose)
pt <- numeric(n.dose)
obs.btox <- numeric(n.dose)
obs.beff <- numeric(n.dose)
dose.curr <- startdose
bgamma <- numeric(n.dose)
d=NULL ####record dose allocation####
t.enter=NULL
localt.enter=NULL
localt.finish=NULL
t.eventT=NULL
t.eventE=NULL
t.onset=NULL
t.finish=NULL
t.curr=0
tite.df <- NULL
nvector=rep(0,n.dose)
bdosekeep=NULL
backfill=0 # how many times of backfill.
backfillvector=rep(0,length=n.dose) ## which dose is backfilled.
elimi_tox<-rep(0,n.dose)
elimi_eff<-rep(0,n.dose)
elimi<-rep(0,n.dose)
earlystop<-0
type=NULL
for(i in 1:ncohort){
### assign three maxpatients to dose.curr
# t.curr; t.finish;t.onset
localt.enter=NULL
for(j in 1:cohortsize){
## local t.enter
if(j==1){
localt.enter=c(localt.enter,t.curr)
}else{
if(gen.enroll.time=="uniform"){ localt.enter =c(localt.enter, localt.enter[length(localt.enter)]+ runif(1, 0, 2/accrual))}
if(gen.enroll.time=="exponential"){ localt.enter = c(localt.enter, localt.enter[length(localt.enter)]+ rexp(1, rate=accrual))}
}
}
localt.finish=localt.enter+tau.T
t.enter=c(t.enter,localt.enter)
t.finish=c(t.finish,localt.finish)
time.te <- copula::rMvdc(cohortsize,mv.ncop[[dose.curr]]) ####event time#####
localt.onset=localt.enter+time.te[,1]
localt.yT=as.numeric(time.te[,1]<=tau.T)
localt.yE=as.numeric(time.te[,2]<=tau.E)
yT=c(yT,as.numeric(time.te[,1]<=tau.T))
yE=c(yE,as.numeric(time.te[,2]<=tau.E))
d=c(d,rep(dose.curr,cohortsize))
type=c(type,rep(1,cohortsize))
t.eventT=c(t.eventT,time.te[,1])
t.eventE=c(t.eventE,time.te[,2])
t.onset=t.enter+apply(data.frame(
t.eventT=t.eventT,
t.eventE=t.eventE
), 1, max, na.rm=TRUE)
endt=apply(data.frame(
endt1=t.enter+tau.T,
endt2=t.enter+t.eventT
), 1, min, na.rm=TRUE)
#####added for more than or equal 1-suspend patients completed the DLT assessment####;
if (suspend!=0){
quantile=quantile(endt[which(d==dose.curr)], probs = (1-suspend))
quantile50=max(endt[which(d==dose.curr)][endt[which(d==dose.curr)]<=quantile])
}
#####end######;
ende=apply(data.frame(
ende1=t.enter+tau.E,
ende2=t.enter+t.eventE
), 1, min, na.rm=TRUE)
nvector[dose.curr]=nvector[dose.curr]+cohortsize
## renew t.curr until there is open doses
flag=0
t.curr=max(t.enter)
if (suspend!=0){
t.bench=min(max(localt.finish),max(localt.onset),quantile50)
}else{
t.bench=min(max(localt.finish),max(localt.onset))
}
if(i==ncohort){t.bench=t.bench+100}
###bacause in the last cohort, we need to enroll enough patients to the backfilling doses, setting as 100 months#######;
queue=NULL
# t.enter;t.onset;t.finish
while(flag==0){
flag=1
if(gen.enroll.time=="uniform"){
t.curr=t.curr+runif(1, 0, 2/accrual)
queue=c(queue,t.curr)
}else if(gen.enroll.time=="exponential"){
t.curr=t.curr+rexp(1, rate=accrual)
queue=c(queue,t.curr)
}
if(t.bench>t.curr){
flag=0
}
}
queue=queue[which(queue!=t.curr)]
db=NULL ##record dose allocation for backfilling
localt.enterb=NULL
localt.finishb=NULL
localt.yTb=NULL
localt.yEb=NULL
time.teba=NULL
titeb.df=NULL
s_back=NULL
# t.curr;queue
# d
## check open doses
## be adviced, we do not eliminate a dose from bf set if it turns of be overtoxic during the trial.
## assume response is immediately available after dose assignment.
## this simplifies the situation, during backfilling, no lower doses watch response
## if not, change the code easily, decide the bfset everytime we queueing
flag2=0
while(flag2==0){
flag2=1
if(dose.curr!=1){ # dynnamically change the bfset as each person arrives.
for (q in queue){
####select max utility score for backfilling dose####;
if(!is.null(bdosekeep)){
if (q!=min(queue)){
#####using the latest information to get the max utility score#####
titeb.df <- data.frame(dose = d,
enter = t.enter,
endtox = endt,
dlt = yT,
endeff = ende,
orr = yE)
for(ds in 1:n.dose){
if(sum(titeb.df$dose==ds)>0){
bcompsub.T <- titeb.df[(titeb.df$endtox<=q)&(titeb.df$dose==ds),]
bpendsub.T <- titeb.df[(titeb.df$endtox >q)&(titeb.df$dose==ds),]
bcompsub.E <- titeb.df[(titeb.df$endeff<=q)&(titeb.df$dose==ds),]
bpendsub.E <- titeb.df[(titeb.df$endeff >q)&(titeb.df$dose==ds),]
bx.DLT <- sum(bcompsub.T$dlt)
bn.DLT <- bx.DLT+sum(1-bcompsub.T$dlt)+sum(q-bpendsub.T$enter)/tau.T
bx.ORR <- sum(bcompsub.E$orr)
bn.ORR <- bx.ORR+sum(1-bcompsub.E$orr)+sum(q-bpendsub.E$enter)/tau.E
obs.tox[ds] <- bx.DLT
obs.tox.n[ds] <- bn.DLT
pt[ds] <- bx.DLT/bn.DLT
obs.eff[ds] <- bx.ORR
obs.eff.n[ds] <- bn.ORR
pe[ds] <- bx.ORR/bn.ORR
titeb.curdose <- titeb.df[titeb.df$dose==ds,]
bgamma.T <- as.numeric(titeb.curdose$endtox<=q)
bgamma.E <- as.numeric(titeb.curdose$endeff<=q)
bgamma[ds]<- mean(bgamma.T*bgamma.E)
}}
}
###select the backfilling dose using utility score#####;
####add utility score#####;
evadose <- intersect(intersect(dosen[nvector!=0],bdosekeep),dosen[nvector<stopping.npts])
if(!identical(evadose, integer(0))) {
estpt <- Iso::pava(pt[evadose])
utility_b<-futility_score(pe=pe[evadose],pt=estpt,psi00=ppsi00,psi01=ppsi01,psi10=ppsi10,psi11=ppsi11)
s_back=max(evadose[utility_b==max(utility_b)]) ###backfilling dose#####;
}else{
s_back=NULL
}
}
## check the status of current dos, if it all finishes and reaches n.stop and decision is stay, terminate the trial
if(!is.null(s_back)){
s=s_back
if(nvector[s]<stopping.npts){
## note, I do not care if toxicity of this backfill dose is currently over toxic or not.
db=c(db,s)
d=c(d,s)
type=c(type,0)
localt.enterb=c(localt.enterb,q)
localt.finishb=c(localt.finishb,q+tau.T)
t.enter=c(t.enter,q)
t.finish=c(t.finish, q+tau.T)
time.teb<-copula::rMvdc(1,mv.ncop[[s]])
time.teba<-rbind(time.teba,time.teb)
localt.yTb=c(localt.yTb,as.numeric(time.teb[,1]<=tau.T))
localt.yEb=c(localt.yEb,as.numeric(time.teb[,2]<=tau.E))
yT=c(yT,as.numeric(time.teb[,1]<=tau.T))
yE=c(yE,as.numeric(time.teb[,2]<=tau.E))
t.eventT=c(t.eventT,time.teb[,1])
t.eventE=c(t.eventE,time.teb[,2])
t.onset=t.enter+apply(data.frame(
t.eventT=t.eventT,
t.eventE=t.eventE
), 1, max, na.rm=TRUE)
endt=apply(data.frame(
endt1=t.enter+tau.T,
endt2=t.enter+t.eventT
), 1, min, na.rm=TRUE)
ende=apply(data.frame(
endeb1=t.enter+tau.E,
endeb2=t.enter+t.eventE
), 1, min, na.rm=TRUE)
nvector[s]=nvector[s]+1
backfillvector[s]=backfillvector[s]+1
backfill=backfill+1
}
}
} # end of queue
# queue;t.enter;t.event;t.onset;t.finish;d;yT;yE;nvector; backfillvector; d
} # dose.curr !=1
}
####decide next dose#####;
tite.df <- rbind(tite.df,
data.frame(dose = dose.curr,
enter = localt.enter,
endtox = localt.enter+apply(as.matrix(1:cohortsize),1,function(x){min(time.te[x,1],tau.T)}),
dlt = localt.yT,
endeff = localt.enter+apply(as.matrix(1:cohortsize),1,function(x){min(time.te[x,2],tau.E)}),
orr = localt.yE,
backfill = rep(0,cohortsize)))
if(!is.null(localt.enterb)){
tite.df <- rbind(tite.df,
data.frame(dose = db,
enter = localt.enterb,
endtox = localt.enterb+apply(as.matrix(1:length(localt.enterb)),1,function(x){min(time.teba[x,1],tau.T)}),
dlt = localt.yTb,
endeff = localt.enterb+apply(as.matrix(1:length(localt.enterb)),1,function(x){min(time.teba[x,2],tau.E)}),
orr = localt.yEb,
backfill = rep(1,length(localt.enterb))))
}
tite.curdose <- tite.df[tite.df$dose==dose.curr,]
gamma.T <- as.numeric(tite.curdose$endtox<=t.curr)
gamma.E <- as.numeric(tite.curdose$endeff<=t.curr)
gamma.all.T <- as.numeric(tite.df$endtox<=t.curr)
gamma.all.E <- as.numeric(tite.df$endeff<=t.curr)
for(ds in 1:n.dose){
if(sum(tite.df$dose==ds)>0){
compsub.T <- tite.df[(tite.df$endtox<=t.curr)&(tite.df$dose==ds),]
pendsub.T <- tite.df[(tite.df$endtox >t.curr)&(tite.df$dose==ds),]
compsub.E <- tite.df[(tite.df$endeff<=t.curr)&(tite.df$dose==ds),]
pendsub.E <- tite.df[(tite.df$endeff >t.curr)&(tite.df$dose==ds),]
x.DLT <- sum(compsub.T$dlt)
n.DLT <- x.DLT+sum(1-compsub.T$dlt)+sum(t.curr-pendsub.T$enter)/tau.T
x.ORR <- sum(compsub.E$orr)
n.ORR <- x.ORR+sum(1-compsub.E$orr)+sum(t.curr-pendsub.E$enter)/tau.E
obs.tox[ds] <- x.DLT
obs.tox.n[ds] <- n.DLT
pt[ds] <- x.DLT/n.DLT
obs.eff[ds] <- x.ORR
obs.eff.n[ds] <- n.ORR
pe[ds] <- x.ORR/n.ORR
#####create two paramters for - Average number of patients who experienced toxicity/efficacy at each dose level in backfill cohort####;
x.bDLT <- sum(compsub.T[compsub.T$backfill==1,]$dlt)
x.bORR <- sum(compsub.E[compsub.E$backfill==1,]$orr)
obs.btox[ds] <- x.bDLT
obs.beff[ds] <- x.bORR
}}
#####dose allocation#####;
if(pt[dose.curr]<=lambda1){
nxtdose <- dose.curr+1
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr){
backdose<-c(jmin:dose.curr)
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}else if( ((pt[dose.curr]>lambda1) & (pt[dose.curr]<=lambda2)) | ( (obs.tox[dose.curr]==1) & (nvector[dose.curr]==3)) ){
nxtdose <- dose.curr
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr-1){
backdose<-c(jmin:(dose.curr-1))
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}else if(pt[dose.curr]>lambda2){
nxtdose <- dose.curr-1
####backfill doses#####;
if(length(dosen[which(pe>=eta1)])>0){
jmin<-min(dosen[which(pe>=eta1)])
}else{
jmin<-NULL
}
if(!is.null(jmin)){
if (jmin<=dose.curr-2){
backdose<-c(jmin:(dose.curr-2))
}else{
backdose<-NULL
}
}else{
backdose<-NULL
}
}
####eliminate dose#####;
po.shape1 <- pr.alpha + obs.tox
po.shape2 <- pr.beta + (nvector-obs.tox)
tterm <- pbeta(target_T,po.shape1,po.shape2)
po.shape1 <- pr.alpha + obs.eff
po.shape2 <- pr.beta + (nvector-obs.eff)
eterm <- 1-pbeta(target_E,po.shape1,po.shape2)
###determine which dose level should be eliminated
if(length(which((tterm<(1-stopping.prob.T))&(nvector>=n.elimination)))>0){
elimi_tox[(min(which((tterm<(1-stopping.prob.T))&(nvector>=n.elimination)))):n.dose]<-1
}
if(length(which((eterm<(1-stopping.prob.E))&(nvector>=n.elimination)))>0){
elimi_eff[which((eterm<(1-stopping.prob.E))&(nvector>=n.elimination))]<-1
}
elimi<-elimi_tox+elimi_eff
admflg <- (elimi==0)
admdose <- dosen[admflg]
if(sum(admflg)==0){
earlystop=1
break
}else if((length(d[which(type==1)]))==nmax){ ## dose-escalation patients are consumed! ##enrollment is stopped when 1)
break
}else if((nvector[dose.curr]>=stopping.npts) & ( ##enrollment is stopped when 2)
((pt[dose.curr]>lambda1) & (pt[dose.curr]<=lambda2)) | (dose.curr==1 & pt[dose.curr]>=lambda2) | ((dose.curr==n.dose||admflg[dose.curr+1]==0) & pt[dose.curr]<=lambda1)
)){
break
}else{
if(nxtdose==0){
if(admflg[1]){
dose.curr <- 1
}else{
break
}
}else if(nxtdose==(n.dose+1)){
if(admflg[n.dose]){
dose.curr <- n.dose
}else{
break
}
}else if(is.element(nxtdose,admdose)){
dose.curr <- nxtdose
}else if(dose.curr<nxtdose){
if(sum(admdose>=nxtdose)!=0){
dose.curr <- min(admdose[admdose>=nxtdose])
}
}else if(dose.curr>=nxtdose){
if(sum(admdose<=nxtdose)!=0){
dose.curr <- max(admdose[admdose<=nxtdose])
}else{
break
}
}
####for backfilling doses#####;
if(!is.null(backdose)){
if(length(intersect(backdose,admdose))>0){
bdosekeep<-intersect(backdose,admdose)
}else{
bdosekeep<-NULL
}
}else{
bdosekeep<-NULL
}
####end;
}
}
t.curr=max(endt,ende)
data.obs.n[simu,] <- nvector
data.dur[simu] <- t.curr
toxprobr[simu,] <- as.vector(t(toxprob))
effprobr[simu,] <- as.vector(t(effprob))
efficacy[simu,]=obs.eff
toxicity[simu,]=obs.tox
befficacy[simu,]=obs.beff
btoxicity[simu,]=obs.btox
backfilltimes[simu]=backfill
backfillcount[simu,]=backfillvector
evadose <- intersect(dosen[nvector!=0],dosen[])
obspt <- obs.tox[evadose]/nvector[evadose]
obspe <- obs.eff[evadose]/nvector[evadose]
tterm.obd <- numeric(n.dose)
eterm.obd <- numeric(n.dose)
for(i in evadose){
po.shape1 <- pr.alpha + obs.tox[i]
po.shape2 <- pr.beta + (nvector[i]-obs.tox[i])
tterm.obd[i] <- pbeta(target_T,po.shape1,po.shape2)
po.shape1 <- pr.alpha + obs.eff[i]
po.shape2 <- pr.beta + (nvector[i]-obs.eff[i])
eterm.obd[i] <- 1-pbeta(target_E,po.shape1,po.shape2)
}
if(length(d)<nmax & earlystop==1){
obd[simu] <- 0
}else if(length(evadose)==1){
if((tterm.obd[evadose]>=(1-stopping.prob.T))&(eterm.obd[evadose]>=(1-stopping.prob.E))){
obd[simu] <- evadose
}
}else if(sum((tterm.obd[evadose]>=(1-stopping.prob.T))&(eterm.obd[evadose]>=(1-stopping.prob.E)))>=1){
estpt <- Iso::pava(obspt)
estpe <- obspe
obd[simu] <- obd.select(probt=estpt, probe=estpe,tterm=tterm.obd[evadose], eterm=eterm.obd[evadose],
stopT=stopping.prob.T, stopE=stopping.prob.E , ph=target_T ,psi00=ppsi00, psi01=ppsi01,psi10=ppsi10,psi11=ppsi11)
}
####output results####;
for(i in 1:n.dose){
prop.select[simu,i] <- round(mean(obd[simu]==i)*100,digits=1)
}
colnames(prop.select) <- dose
prop.stop[simu] <- round(mean(obd[simu]==0)*100,digits=1)
}
n.patient <- round(apply(data.obs.n,2,mean),digits=2)
names(n.patient) <- dose
n.bpatient <- round(apply(backfillcount,2,mean),digits=2)
names(n.bpatient) <- dose
n.tox.patient <- round(apply(toxicity,2,mean),digits=2)
names(n.tox.patient) <- dose
n.eff.patient <- round(apply(efficacy,2,mean),digits=2)
names(n.eff.patient) <- dose
n.tox.bpatient <- round(apply(btoxicity,2,mean),digits=2)
names(n.tox.bpatient) <- dose
n.eff.bpatient <- round(apply(befficacy,2,mean),digits=2)
names(n.eff.bpatient) <- dose
names(toxprob) <- dose
names(effprob) <- dose
duration <- round(mean(data.dur),digits=1)
names(duration) <- "Trial duration"
totaln = sum(data.obs.n)/an.sim
names(totaln)<-"Total patients"
names(target_T) <- "Target toxicity prob."
names(target_E) <- "Target efficacy prob."
names(lambda1) <- "Lower toxicity boundary"
names(lambda2) <- "Upper toxicity boundary"
names(eta1) <- "Lower efficacy boundary"
names(tau.T) <- "Tox. assessment window (months)"
names(tau.E) <- "Eff. assessment window (months)"
names(accrual) <- "Accrual rate (months)"
names(suspend) <- "Suspension rule"
names(target_Tr) <- "The upper boundary for the toxicity"
names(target_Er) <- "The lower boundary for the efficacy"
colnames(toxprobr) <- dose
colnames(effprobr) <- dose
colnames(data.obs.n) <- dose
colnames(backfillcount) <- dose
PCSA <- array(0,dim=c(an.sim))
PCAA <- array(0,dim=c(an.sim))
POAA <- array(0,dim=c(an.sim))
POS=sum(obd>mtd.true)*100/an.sim
for (k in 1:an.sim){
if(bd[k]==0){
PCSA[k]=prop.stop[k]
PCAA[k]=0
}else{
PCSA[k]=prop.select[k,bd[k]]
PCAA[k]=(data.obs.n[k,]*100/sum(data.obs.n[k,]))[bd[k]]
}
if (mtd.true[k]==n.dose){POAA[k]=0}else{
POAA[k]= sum(data.obs.n[k,(mtd.true[k]+1):n.dose])*100/sum(data.obs.n[k,])# percent of overdose allocation
}
}
POA=round(mean(POAA),digits=1)
PCS=round(mean(PCSA),digits=1)
PCA=round(mean(PCAA),digits=1)
names(PCS) <-"The percentage of trials that the optimal dose was correctly selected"
names(PCA) <-"The percentage of patients that were correctly allocated to the optimal dose"
names(POS) <-"The percentage of toxic doses selection"
names(POA) <-"The percentage of patients who were allocated to toxic doses"
result <- list(toxprob = toxprobr,
effprob = effprobr,
phi = target_T,
delta = target_E,
target_Tr = target_Tr,
target_Er = target_Er,
bd.true = bd,
mtd.true = mtd.true,
lambda1 = lambda1,
lambda2 = lambda2,
eta1 = eta1,
tau.T = tau.T,
tau.E = tau.E,
suspend = suspend,
accrual = accrual,
n.patient = n.patient,
n.bpatient = n.bpatient,
n.tox.patient= n.tox.patient,
n.eff.patient= n.eff.patient,
n.tox.bpatient= n.tox.bpatient,
n.eff.bpatient= n.eff.bpatient,
prop.select = prop.select,
prop.stop = prop.stop,
duration = duration,
totaln = totaln,
data.obs.n = data.obs.n,
obd = obd,
backfilltimes= backfilltimes,
backfillcount= backfillcount,
PCS=PCS,
PCA=PCA,
PTS=POS,
PTA=POA
)
class(result) <- "BF-BOIN-ET-R"
return(result)
}
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