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#' @title Simulate full trial (both stages) x times
#'
#' @description Results are displayed in a matrix format, where each row
#' represents one trial simulation.
#'
#' @return List of the following objects:
#' \itemize{
#' \item sim.Y - estimated efficacy per each dose assignment
#' \item sim.d - dose assignment for each patient in the trial
#' \item safe.d - indicator of whether dose was declared safe
#' }
#'
#'
#' @param numsims number of simulated trials
#' @param dose number of doses to be tested (scalar)
#' @param dose.tox vector of true toxicities for each dose. Values range from 0 - 1.
#' @param p1 toxicity under null (unsafe DLT rate). Values range from 0 - 1.
#' @param p2 toxicity under alternative (safe DLT rate). Values range from 0 - 1; p1 > p2
#' @param K threshold for LR. Takes integer values: 1,2,...(recommended K=2)
#' @param coh.size cohort size (number of patients) per dose (Stage 1)
#' @param m vector of mean efficacies per dose. Values range from 0 - 100.
#' (e.g, T cell persistence - values b/w 5 and 80 per cent)
#' @param v vector of efficacy variances per dose. Values range from 0 - 1. (e.g., 0.01)
#' @param nbb binomial parameter (default = 100 cells per patient)
#' @param N total sample size for stages 1&2
#' @param stop.rule if only dose 1 safe, allocate up to 9 (default) patients at dose 1
#' to collect more info.
#' @param cohort cohort size (number of patients) per dose (Stage 2). Default is 1.
#' @param samedose designates whether the next patient is allocated to the same dose
#' as the previous patient. Default is TRUE. Function adjusts accordingly.
#'
#' @examples
#' # Number of pre-specified dose levels
#' dose <- 5
#'
#' # Vector of true toxicities associated with each dose
#' dose.tox <- c(0.05, 0.10, 0.20, 0.35, 0.45)
#'
#' # Acceptable (p_yes) and unacceptable (p_no) DLT rates used for establishing safety
#' p_no <- 0.40
#' p_yes <- 0.15
#'
#' # Likelihood-ratio (LR) threshold
#' K <- 2
#'
#' # Cohort size used in stage 1
#' coh.size <- 3
#'
#' # Vector of true mean efficacies per dose (here mean percent persistence per dose)
#' m <- c(5, 15, 40, 65, 80) # MUST BE THE SAME LENGTH AS dose.tox
#'
#' # Efficacy(equal) variance per dose
#' v <- rep(0.01, 5)
#'
#' # Total sample size (stages 1&2)
#' N <- 25
#'
#' # Stopping rule: if dose 1 is the only safe dose, allocate up to 9 pts.
#' stop.rule <- 9
#'
#' sim.trials(numsims = 10, dose, dose.tox, p1 = p_no, p2 = p_yes, K,
#' coh.size, m, v, N, stop.rule = stop.rule, cohort = 1, samedose = TRUE, nbb = 100)
#'
#' @export
sim.trials <- function(numsims, dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule = 9,
cohort = 1, samedose = TRUE, nbb = 100){
sim.yk <- sim.dk <- matrix(NA, nrow = numsims, ncol = N)
sim.doses <- matrix(NA, nrow = numsims, ncol = dose)
## add progress bar to output
pb <- utils::txtProgressBar(min = 0, # Minimum value of the progress bar
max = numsims, # Maximum value of the progress bar
style = 3, # Progress bar style (also available style = 1 and style = 2)
width = 50, # Progress bar width. Defaults to getOption("width")
char = "=") # Character used to create the bar
for (i in 1:numsims) {
fstudy.out <- rand.stg2(dose, dose.tox, p1, p2, K, coh.size, m, v, N, stop.rule, cohort, samedose, nbb)
# number of doses declared safe, based on stage 1 sample size
n.safe <- max(fstudy.out$d.final[(fstudy.out$n1 + 1):length(fstudy.out$d.final)], na.rm = TRUE)
sim.doses[i,] <- c(rep(1, n.safe), rep(0, dose - n.safe)) # binary vector for dose safety
if (length(fstudy.out$Y.final) < N) { # if max sample size was not reached, fill-in with NAs
sim.yk[i,] <- c(fstudy.out$Y.final, rep(NA, N - length(fstudy.out$Y.final)))
sim.dk[i,] <- c(fstudy.out$d.final, rep(NA, N - length(fstudy.out$d.final)))
} else {
sim.yk[i,] <- fstudy.out$Y.final
sim.dk[i,] <- fstudy.out$d.final
}
# Sets the progress bar to the current state
utils::setTxtProgressBar(pb, i)
# cat(i,"\n")
}
return(list(sim.Y = sim.yk,
sim.d = sim.dk,
safe.d = sim.doses))
}
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