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#' @import ggplot2
#' @import rstan
#' @import Rcpp
#' @import methods
#' @import stats
#' @useDynLib dfpk, .registration = TRUE
#' @export
dtox <-
function(y, doses, x, theta, prob = 0.9, options=list(nchains = 4, niter = 4000, nadapt = 0.8),
betapriors = c(0, 16.71, 0, 6.43), thetaL=NULL, auc = NULL, deltaAUC = NULL, p0 = NULL, L = NULL, CI = TRUE){
checking1 <- function(x,target,error){
sum(x>(target+error))/length(x)
}
num <- length(x) # how many patients
dose1 <- log(doses[x])
# For STAN model
data_s <- list(N=num, y=y, dose=dose1, beta0mean=c(betapriors[1], betapriors[2]), beta1mean=c(betapriors[3], betapriors[4]))
sm_lrDtox <- stanmodels$cdf_reg_dtox
reg1 <- sampling(sm_lrDtox, data=data_s, iter=options$niter, chains=options$nchains, control = list(adapt_delta = options$nadapt))
a1 = get_posterior_mean(reg1)
sampl1 <- extract(reg1)
beta <- a1[1:2, options$nchains + 1]
beta0 <- -beta[1]
beta1 <- beta[2]
pnew <- pnorm(beta0 + beta1*log(doses))
Beta0 <- -sampl1$beta0
Beta1 <- sampl1$beta1
pstim_sum <- matrix(0, ncol = options$nchains*options$niter/2, nrow = length(doses))
p_sum <- NULL
for(i in 1:ncol(pstim_sum)){
pstim_sum[1,i] <- pnorm(Beta0[i] + Beta1[i]*log(doses[1]))
}
#######################
#### Stopping Rule ####
#######################
pstop <- checking1(pstim_sum[1,], target=theta, error=0)
stoptox <- (pstop >= prob)
stoptrial <- stoptox
if(CI == "TRUE"){
p_sum <- summary(pstim_sum[1,])
for(o in 2:length(doses)){
for(i in 1:ncol(pstim_sum)){
pstim_sum[o,i] <- pnorm(Beta0[i] + Beta1[i]*log(doses[o]))
}
p_sum <- rbind(p_sum, summary(pstim_sum[o,]))
}
}else{
p_sum <- NULL
}
# check if we will stop the trial or not
if (stoptrial){
newDose = NA
message("The trial stopped based on the stopping rule \n \n")
}else{ # if we don't stopped
newDose <- order(abs(pnew-theta))[1]
}
parameters <- beta
names(parameters) <- c("beta0", "beta1")
list(newDose = newDose, pstim = pnew, p_sum = p_sum, parameters = parameters)
}
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