Nothing
R/CFOeff.simu.R
In CFO: CFO-Type Designs in Phase I/II Clinical Trials
Defines functions
CFOeff.simu
Documented in
CFOeff.simu
#' Conduct one simulation using the calibration-free odds (CFO) design for phase I/II trials
#'
#' In the CFO design for phase I/II trials, the function is used to conduct one single simulation and find the optimal biological dose (OBD).
#' @usage CFOeff.simu(target, p.true, pE.true, ncohort=10, init.level=1, cohortsize=3,
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5),
#' cutoff.eli = 0.95, early.stop = 0.95,
#' effearly.stop = 0.9, mineff, seed = NULL)
#' @param target the target DLT rate.
#' @param p.true the true DLT rates under the different dose levels.
#' @param pE.true the true efficacy rates under the different dose levels.
#' @param ncohort the total number of cohorts.
#' @param init.level the dose level assigned to the first cohort. The default value of \code{init.level} is 1.
#' @param cohortsize the number of patients of each cohort.
#' @param prior.para the prior parameters for two beta distributions, where set as \code{list(alp.prior = target,
#' bet.prior = 1 - target, alp.prior.eff = 0.5, bet.prior.eff = 0.5)} by default. \code{alp.prior} and \code{bet.prior}
#' represent the parameters of the prior distribution for the true DLT rate at any dose level. This prior distribution
#' is specified as Beta(\code{alpha.prior}, \code{beta.prior}). \code{alp.eff.prior} and \code{bet.eff.prior}
#' represent the parameters of the Jeffreys' prior distribution for the efficacy probability at any dose level.
#' This prior distribution is specified as Beta(\code{alpha.eff.prior}, \code{beta.eff.prior}).
#' @param cutoff.eli the cutoff to eliminate overly toxic doses for safety. We recommend
#' the default value of \code{cutoff.eli = 0.95} for general use.
#' @param early.stop the threshold value for early stopping due to overly toxic. The default value \code{early.stop = 0.95}
#' generally works well.
#' @param effearly.stop the threshold value for early stopping due to low efficacy. The trial would be terminated
#' early if \eqn{Pr(q_k<\psi |y_k,m_k \ge 3)} is smaller than the value of \code{effearly.stop} where \eqn{q_k, y_k} and \eqn{m_k}
#' are the efficacy probability, the number of efficacy outcomes and the number of patients at dose level \eqn{k}.
#' \eqn{\psi} is the the lowest acceptable efficacy rate which is set by \code{mineff} here.
#' By default, \code{effearly.stop} is set as \code{0.9}.
#' @param mineff the lowest acceptable efficacy rate.
#' @param seed an integer to be set as the seed of the random number generator for reproducible results. The default value is set to \code{NULL}.
#' @note The \code{CFOeff.simu} function is designed to conduct a single CFO simulation for phase I/II trials. The dose elimination rule is the
#' same as the case in phase I (refer to the function \code{CFO.simu}). As for early stopping rule, compared to the case of phase I, the rule
#' in this case further considers the efficacy data to terminate the trial early if none of the admissible dose levels show adequate
#' efficacious effect.
#' @return The \code{CFOeff.simu} function returns a list object comprising the following components:
#' \itemize{
#' \item OBD: the selected OBD. \code{OBD = 99} indicates that the simulation is terminated due to early stopping.
#' \item target: the target DLT rate.
#' \item npatients: the total number of patients allocated to all dose levels.
#' \item neff: the total number of efficacy outcomes for all dose levels.
#' \item ntox: the total number of DLTs observed for all dose levels.
#' \item pE.true: the true efficacy rates under the different dose levels.
#' \item p.true: the true DLT rates under the different dose levels.
#' \item cohortdose: a vector including the dose level assigned to each cohort.
#' \item ptoxic: the percentage of subjects assigned to dose levels with a DLT rate greater than the target.
#' \item patientDLT: a vector including the DLT outcome observed for each patient.
#' \item patienteff: a vector including the efficacy outcome observed for each patient.
#' \item over.doses: a vector indicating whether each dose is overdosed or not (1 for yes).
#' \item under.eff: a vector indicating whether the efficacy of each dose is lower than
#' acceptable efficacy rate (1 for yes).
#' \item correct: a binary indicator of whether the recommended dose level matches the correct OBD (1 for yes).
#' The correct OBD is the dose level in the admissible set with the upper bound being the correct MTD,
#' which has the highest true efficacy probability.
#' \item OBDprob: the probability that each dose level would be selected as OBD. The probability indicates that \eqn{q_k} corresponds
#' to dose level \eqn{k} being the highest in the admissible set. \eqn{q_k} is efficacy probability correspond to dose level k here.
#' \item sumDLT: the total number of DLT observed.
#' \item sumeff: the total number of efficacy outcome observed.
#' \item earlystop: a binary indicator of whether the trial is early stopped (1 for yes).
#' \item stopreason: the reason for earlystop. \code{overly_toxic} represents the trial was terminated
#' beacuse all tested doses were overly toxic. \code{low_efficacy} represents the trial was terminated
#' because all tested doses show low efficacy.
#' \item class: the phase of the trial.
#' }
#'
#' @author Jialu Fang, Ninghao Zhang, Wenliang Wang, and Guosheng Yin
#' @references Jin H, Yin G (2022). CFO: Calibration-free odds design for phase I/II clinical trials.
#' \emph{Statistical Methods in Medical Research}, 31(6), 1051-1066. \cr
#' @export
#'
#'
#' @examples
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
#' pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' \donttest{### overly toxic
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.55, 0.57, 0.61, 0.62, 0.66)
#' pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' }
#' \donttest{### low efficacy
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
#' pE.true=c(0.001, 0.003, 0.004, 0.005, 0.006)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' }
CFOeff.simu <- function(target, p.true, pE.true, ncohort=10, init.level=1, cohortsize=3,
prior.para = list(alp.prior = target, bet.prior = 1 - target, alp.prior.eff = 0.5,
bet.prior.eff = 0.5), cutoff.eli=0.95, early.stop=0.95,
effearly.stop = 0.9, mineff, seed = NULL) {
###############################################################################
###############define the functions used for main function#####################
###############################################################################
post.prob.fn <- function(target, y, n, alp.prior=0.1, bet.prior=0.1){
if(n != 0){
alp <- alp.prior + y
bet <- bet.prior + n - y
res <- 1 - pbeta(target, alp, bet)
}else{
res <- NA
}
return(res)
}
under.eff.fn <- function(mineff, effearly.stop,prior.para=list())
{
args <- c(list(target = mineff), prior.para)
x <- prior.para$x
n <- prior.para$n
alp.prior <- prior.para$alp.prior.eff
bet.prior <- prior.para$bet.prior.eff
ppE <- 1 - post.prob.fn(mineff, x, n, alp.prior, bet.prior)
if ((ppE >= effearly.stop) & (n >= 3)) {
return(TRUE)
}else{
return(FALSE)
}
}
OBD.level <- function(phi, mineff, p.true, pE.true) {
if (p.true[1] > phi + 0.1) {
OBD <- 99
return(OBD)
}
OBD <- which.min(abs(phi - p.true))
eff.idxs <- mineff > pE.true[1:OBD]
if (sum(eff.idxs) == OBD) {
OBD <- 99
return(OBD)
}
OBD <- which.max(pE.true[1:OBD])
return(OBD)
}
# compute the marginal prob when lower < phiL/phiC/phiR < upper
# i.e., Pr(Y=y|lower<target<upper)
overdose.fn <- function(target, threshold, prior.para=list()){
y <- prior.para$y
n <- prior.para$n
alp.prior <- prior.para$alp.prior
bet.prior <- prior.para$bet.prior
pp <- post.prob.fn(target, y, n, alp.prior, bet.prior)
if ((pp >= threshold) & (prior.para$n >= 3)) {
return(TRUE)
}else{
return(FALSE)
}
}
moveprobs <- function(ad.xs, ad.ns, alp.prior, bet.prior){
alps <- ad.xs + alp.prior
bets <- ad.ns - ad.xs + bet.prior
nd <- length(ad.xs)
Nsps <- 10000
sps.list <- list()
for (i in 1:nd){
sps.list[[i]] <- rbeta(Nsps, alps[i], bets[i])
}
spss <- do.call(rbind, sps.list)
argMaxs <- apply(spss, 2, which.max)
probs <- as.vector(table(argMaxs))/Nsps
probs
}
###############################################################################
############################MAIN DUNCTION######################################
###############################################################################
# target: Target DIL rate
# mineff: The minimal efficacy rate, only used for early stop
# p.true: True DIL rates under the different dose levels
# pE.true: True efficacy probs under the different dose levels
# ncohort: The number of cohorts
# cohortsize: The sample size in each cohort
# alp.prior, bet.prior: prior parameters
set.seed(seed)
tadd.args <- prior.para
earlystop <- 0
stopreason <- NULL
ndose <- length(p.true)
doselist <- rep(0, ncohort)
cidx <- init.level
OBDprob <- NULL
tys <- rep(0, ndose) # number of DLT responses for different doses.
txs <- rep(0, ndose) # number of efficacy responses for different doses.
tns <- rep(0, ndose) # number of subject for different doses.
tover.doses <- rep(0, ndose) # Whether each dose is too toxic or not, 1 yes.
tunder.effs <- rep(0, ndose) # Whether the dose is not efficacious or not, 1 yes
DLTlist <- c()
EFFlist <- c()
# if a dose is not efficacious enough or it is too toxic, it is would be eliminated from the admissible set.
for (i in 1:ncohort) {
pc <- p.true[cidx]
pEc <- pE.true[cidx]
doselist[i] <- cidx
# sample from current dose
cres <- rbinom(cohortsize, 1, pc)
cEres <- rbinom(cohortsize, 1, pEc)
DLTlist <- c(DLTlist, cres)
EFFlist <- c(EFFlist, cEres)
# update results
tys[cidx] <- tys[cidx] + sum(cres)#The number of observed DLT
txs[cidx] <- txs[cidx] + sum(cEres)#The number of efficacy outcomes
tns[cidx] <- tns[cidx] + cohortsize#The number of patient at dose level k
cy <- tys[cidx]
cx <- txs[cidx]
cn <- tns[cidx]
prior.para <- c(list(y = cy, n = cn, x = cx, tys = tys, txs = txs, tns = tns, cidx = cidx), tadd.args)
if (overdose.fn(target, cutoff.eli, prior.para)) {
tover.doses[cidx:ndose] <- 1
}
if (under.eff.fn(mineff, effearly.stop, prior.para)) {
tunder.effs[cidx] <- 1
}else{
tunder.effs[cidx] <- 0
}
if (tover.doses[1] == 1) {
stopreason <- "overly_toxic"
earlystop <- 1
break()
}
if (sum(tunder.effs[tover.doses == 0]) == sum(tover.doses == 0)){
stopreason <- "low_efficacy"
earlystop <- 1
break()
}
nextinfo <- CFOeff.next(target, axs = txs, ays = tys, ans = tns, cidx, prior.para, cutoff.eli, early.stop, effearly.stop, mineff)
cidx <- nextinfo$nextdose
if (cidx == 99){
if (nextinfo$decision == "stop_for_tox"){
stopreason <- "overly_toxic"
}else{
stopreason <- "low_efficacy"
}
earlystop <- 1
break()
}
if (i == ncohort){
para = list(alp.prior.eff = prior.para$alp.prior.eff, bet.prior.eff = prior.para$bet.prior.eff)
result <- CFOeff.selectobd(target, txs, tys, tns, para, mineff, effearly.stop)
OBDprob <- result$OBD.probs
}
}
if (earlystop == 0) {
MTD <- CFO.selectmtd(target, tns, tys)$MTD
if ( (MTD == 99) | (sum(tunder.effs[1:MTD]) == MTD)) {
OBD <- 99
}else{
OBD.probs <- moveprobs(txs[1:MTD], tns[1:MTD], prior.para$alp.prior.eff, prior.para$bet.prior.eff)
OBD <- which.max(OBD.probs)
}
}else{
OBD <- 99
}
tobd = OBD.level(target, mineff, p.true, pE.true)
correct <- 0
if (OBD == tobd){
correct <- 1
}
ptoxic <- sum(tns[which(p.true > target)])/(ncohort*cohortsize)
out <- list(OBD = OBD, target = target, npatients = tns, neff = txs, ntox = tys, pE.true = pE.true, p.true = p.true,
cohortdose = doselist, ptoxic = ptoxic, patientDLT = DLTlist, patienteff = EFFlist,
over.doses = tover.doses, under.eff = tunder.effs, correct = correct, OBDprob = OBDprob,
sumDLT = sum(DLTlist), sumeff = sum(EFFlist), earlystop = earlystop, stopreason = stopreason,
class = "phaseI/II")
class(out) <- c("cfo_eff_trial", "cfo")
return(out)
}
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Defines functions CFOeff.simu
Documented in CFOeff.simu
#' Conduct one simulation using the calibration-free odds (CFO) design for phase I/II trials
#'
#' In the CFO design for phase I/II trials, the function is used to conduct one single simulation and find the optimal biological dose (OBD).
#' @usage CFOeff.simu(target, p.true, pE.true, ncohort=10, init.level=1, cohortsize=3,
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5),
#' cutoff.eli = 0.95, early.stop = 0.95,
#' effearly.stop = 0.9, mineff, seed = NULL)
#' @param target the target DLT rate.
#' @param p.true the true DLT rates under the different dose levels.
#' @param pE.true the true efficacy rates under the different dose levels.
#' @param ncohort the total number of cohorts.
#' @param init.level the dose level assigned to the first cohort. The default value of \code{init.level} is 1.
#' @param cohortsize the number of patients of each cohort.
#' @param prior.para the prior parameters for two beta distributions, where set as \code{list(alp.prior = target,
#' bet.prior = 1 - target, alp.prior.eff = 0.5, bet.prior.eff = 0.5)} by default. \code{alp.prior} and \code{bet.prior}
#' represent the parameters of the prior distribution for the true DLT rate at any dose level. This prior distribution
#' is specified as Beta(\code{alpha.prior}, \code{beta.prior}). \code{alp.eff.prior} and \code{bet.eff.prior}
#' represent the parameters of the Jeffreys' prior distribution for the efficacy probability at any dose level.
#' This prior distribution is specified as Beta(\code{alpha.eff.prior}, \code{beta.eff.prior}).
#' @param cutoff.eli the cutoff to eliminate overly toxic doses for safety. We recommend
#' the default value of \code{cutoff.eli = 0.95} for general use.
#' @param early.stop the threshold value for early stopping due to overly toxic. The default value \code{early.stop = 0.95}
#' generally works well.
#' @param effearly.stop the threshold value for early stopping due to low efficacy. The trial would be terminated
#' early if \eqn{Pr(q_k<\psi |y_k,m_k \ge 3)} is smaller than the value of \code{effearly.stop} where \eqn{q_k, y_k} and \eqn{m_k}
#' are the efficacy probability, the number of efficacy outcomes and the number of patients at dose level \eqn{k}.
#' \eqn{\psi} is the the lowest acceptable efficacy rate which is set by \code{mineff} here.
#' By default, \code{effearly.stop} is set as \code{0.9}.
#' @param mineff the lowest acceptable efficacy rate.
#' @param seed an integer to be set as the seed of the random number generator for reproducible results. The default value is set to \code{NULL}.
#' @note The \code{CFOeff.simu} function is designed to conduct a single CFO simulation for phase I/II trials. The dose elimination rule is the
#' same as the case in phase I (refer to the function \code{CFO.simu}). As for early stopping rule, compared to the case of phase I, the rule
#' in this case further considers the efficacy data to terminate the trial early if none of the admissible dose levels show adequate
#' efficacious effect.
#' @return The \code{CFOeff.simu} function returns a list object comprising the following components:
#' \itemize{
#' \item OBD: the selected OBD. \code{OBD = 99} indicates that the simulation is terminated due to early stopping.
#' \item target: the target DLT rate.
#' \item npatients: the total number of patients allocated to all dose levels.
#' \item neff: the total number of efficacy outcomes for all dose levels.
#' \item ntox: the total number of DLTs observed for all dose levels.
#' \item pE.true: the true efficacy rates under the different dose levels.
#' \item p.true: the true DLT rates under the different dose levels.
#' \item cohortdose: a vector including the dose level assigned to each cohort.
#' \item ptoxic: the percentage of subjects assigned to dose levels with a DLT rate greater than the target.
#' \item patientDLT: a vector including the DLT outcome observed for each patient.
#' \item patienteff: a vector including the efficacy outcome observed for each patient.
#' \item over.doses: a vector indicating whether each dose is overdosed or not (1 for yes).
#' \item under.eff: a vector indicating whether the efficacy of each dose is lower than
#' acceptable efficacy rate (1 for yes).
#' \item correct: a binary indicator of whether the recommended dose level matches the correct OBD (1 for yes).
#' The correct OBD is the dose level in the admissible set with the upper bound being the correct MTD,
#' which has the highest true efficacy probability.
#' \item OBDprob: the probability that each dose level would be selected as OBD. The probability indicates that \eqn{q_k} corresponds
#' to dose level \eqn{k} being the highest in the admissible set. \eqn{q_k} is efficacy probability correspond to dose level k here.
#' \item sumDLT: the total number of DLT observed.
#' \item sumeff: the total number of efficacy outcome observed.
#' \item earlystop: a binary indicator of whether the trial is early stopped (1 for yes).
#' \item stopreason: the reason for earlystop. \code{overly_toxic} represents the trial was terminated
#' beacuse all tested doses were overly toxic. \code{low_efficacy} represents the trial was terminated
#' because all tested doses show low efficacy.
#' \item class: the phase of the trial.
#' }
#'
#' @author Jialu Fang, Ninghao Zhang, Wenliang Wang, and Guosheng Yin
#' @references Jin H, Yin G (2022). CFO: Calibration-free odds design for phase I/II clinical trials.
#' \emph{Statistical Methods in Medical Research}, 31(6), 1051-1066. \cr
#' @export
#'
#'
#' @examples
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
#' pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' \donttest{### overly toxic
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.55, 0.57, 0.61, 0.62, 0.66)
#' pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' }
#' \donttest{### low efficacy
#' target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; init.level = 1
#' prior.para = list(alp.prior = target, bet.prior = 1 - target,
#' alp.prior.eff = 0.5, bet.prior.eff = 0.5)
#' p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
#' pE.true=c(0.001, 0.003, 0.004, 0.005, 0.006)
#' result <- CFOeff.simu(target, p.true, pE.true, ncohort, init.level, cohortsize,
#' prior.para, mineff = mineff, seed = 1)
#' summary(result)
#' plot(result)
#' }
CFOeff.simu <- function(target, p.true, pE.true, ncohort=10, init.level=1, cohortsize=3,
prior.para = list(alp.prior = target, bet.prior = 1 - target, alp.prior.eff = 0.5,
bet.prior.eff = 0.5), cutoff.eli=0.95, early.stop=0.95,
effearly.stop = 0.9, mineff, seed = NULL) {
###############################################################################
###############define the functions used for main function#####################
###############################################################################
post.prob.fn <- function(target, y, n, alp.prior=0.1, bet.prior=0.1){
if(n != 0){
alp <- alp.prior + y
bet <- bet.prior + n - y
res <- 1 - pbeta(target, alp, bet)
}else{
res <- NA
}
return(res)
}
under.eff.fn <- function(mineff, effearly.stop,prior.para=list())
{
args <- c(list(target = mineff), prior.para)
x <- prior.para$x
n <- prior.para$n
alp.prior <- prior.para$alp.prior.eff
bet.prior <- prior.para$bet.prior.eff
ppE <- 1 - post.prob.fn(mineff, x, n, alp.prior, bet.prior)
if ((ppE >= effearly.stop) & (n >= 3)) {
return(TRUE)
}else{
return(FALSE)
}
}
OBD.level <- function(phi, mineff, p.true, pE.true) {
if (p.true[1] > phi + 0.1) {
OBD <- 99
return(OBD)
}
OBD <- which.min(abs(phi - p.true))
eff.idxs <- mineff > pE.true[1:OBD]
if (sum(eff.idxs) == OBD) {
OBD <- 99
return(OBD)
}
OBD <- which.max(pE.true[1:OBD])
return(OBD)
}
# compute the marginal prob when lower < phiL/phiC/phiR < upper
# i.e., Pr(Y=y|lower<target<upper)
overdose.fn <- function(target, threshold, prior.para=list()){
y <- prior.para$y
n <- prior.para$n
alp.prior <- prior.para$alp.prior
bet.prior <- prior.para$bet.prior
pp <- post.prob.fn(target, y, n, alp.prior, bet.prior)
if ((pp >= threshold) & (prior.para$n >= 3)) {
return(TRUE)
}else{
return(FALSE)
}
}
moveprobs <- function(ad.xs, ad.ns, alp.prior, bet.prior){
alps <- ad.xs + alp.prior
bets <- ad.ns - ad.xs + bet.prior
nd <- length(ad.xs)
Nsps <- 10000
sps.list <- list()
for (i in 1:nd){
sps.list[[i]] <- rbeta(Nsps, alps[i], bets[i])
}
spss <- do.call(rbind, sps.list)
argMaxs <- apply(spss, 2, which.max)
probs <- as.vector(table(argMaxs))/Nsps
probs
}
###############################################################################
############################MAIN DUNCTION######################################
###############################################################################
# target: Target DIL rate
# mineff: The minimal efficacy rate, only used for early stop
# p.true: True DIL rates under the different dose levels
# pE.true: True efficacy probs under the different dose levels
# ncohort: The number of cohorts
# cohortsize: The sample size in each cohort
# alp.prior, bet.prior: prior parameters
set.seed(seed)
tadd.args <- prior.para
earlystop <- 0
stopreason <- NULL
ndose <- length(p.true)
doselist <- rep(0, ncohort)
cidx <- init.level
OBDprob <- NULL
tys <- rep(0, ndose) # number of DLT responses for different doses.
txs <- rep(0, ndose) # number of efficacy responses for different doses.
tns <- rep(0, ndose) # number of subject for different doses.
tover.doses <- rep(0, ndose) # Whether each dose is too toxic or not, 1 yes.
tunder.effs <- rep(0, ndose) # Whether the dose is not efficacious or not, 1 yes
DLTlist <- c()
EFFlist <- c()
# if a dose is not efficacious enough or it is too toxic, it is would be eliminated from the admissible set.
for (i in 1:ncohort) {
pc <- p.true[cidx]
pEc <- pE.true[cidx]
doselist[i] <- cidx
# sample from current dose
cres <- rbinom(cohortsize, 1, pc)
cEres <- rbinom(cohortsize, 1, pEc)
DLTlist <- c(DLTlist, cres)
EFFlist <- c(EFFlist, cEres)
# update results
tys[cidx] <- tys[cidx] + sum(cres)#The number of observed DLT
txs[cidx] <- txs[cidx] + sum(cEres)#The number of efficacy outcomes
tns[cidx] <- tns[cidx] + cohortsize#The number of patient at dose level k
cy <- tys[cidx]
cx <- txs[cidx]
cn <- tns[cidx]
prior.para <- c(list(y = cy, n = cn, x = cx, tys = tys, txs = txs, tns = tns, cidx = cidx), tadd.args)
if (overdose.fn(target, cutoff.eli, prior.para)) {
tover.doses[cidx:ndose] <- 1
}
if (under.eff.fn(mineff, effearly.stop, prior.para)) {
tunder.effs[cidx] <- 1
}else{
tunder.effs[cidx] <- 0
}
if (tover.doses[1] == 1) {
stopreason <- "overly_toxic"
earlystop <- 1
break()
}
if (sum(tunder.effs[tover.doses == 0]) == sum(tover.doses == 0)){
stopreason <- "low_efficacy"
earlystop <- 1
break()
}
nextinfo <- CFOeff.next(target, axs = txs, ays = tys, ans = tns, cidx, prior.para, cutoff.eli, early.stop, effearly.stop, mineff)
cidx <- nextinfo$nextdose
if (cidx == 99){
if (nextinfo$decision == "stop_for_tox"){
stopreason <- "overly_toxic"
}else{
stopreason <- "low_efficacy"
}
earlystop <- 1
break()
}
if (i == ncohort){
para = list(alp.prior.eff = prior.para$alp.prior.eff, bet.prior.eff = prior.para$bet.prior.eff)
result <- CFOeff.selectobd(target, txs, tys, tns, para, mineff, effearly.stop)
OBDprob <- result$OBD.probs
}
}
if (earlystop == 0) {
MTD <- CFO.selectmtd(target, tns, tys)$MTD
if ( (MTD == 99) | (sum(tunder.effs[1:MTD]) == MTD)) {
OBD <- 99
}else{
OBD.probs <- moveprobs(txs[1:MTD], tns[1:MTD], prior.para$alp.prior.eff, prior.para$bet.prior.eff)
OBD <- which.max(OBD.probs)
}
}else{
OBD <- 99
}
tobd = OBD.level(target, mineff, p.true, pE.true)
correct <- 0
if (OBD == tobd){
correct <- 1
}
ptoxic <- sum(tns[which(p.true > target)])/(ncohort*cohortsize)
out <- list(OBD = OBD, target = target, npatients = tns, neff = txs, ntox = tys, pE.true = pE.true, p.true = p.true,
cohortdose = doselist, ptoxic = ptoxic, patientDLT = DLTlist, patienteff = EFFlist,
over.doses = tover.doses, under.eff = tunder.effs, correct = correct, OBDprob = OBDprob,
sumDLT = sum(DLTlist), sumeff = sum(EFFlist), earlystop = earlystop, stopreason = stopreason,
class = "phaseI/II")
class(out) <- c("cfo_eff_trial", "cfo")
return(out)
}
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