#' Empirically determine the operating characteristics of a multi-stage
#' group-sequential multi-arm clinical trial for a normally distributed primary
#' outcome
#'
#' \code{sim_gs_norm()} determines the operating characteristics of a specified
#' multi-stage group-sequential multi-arm clinical trial design assuming the
#' primary outcome is normally distributed, for given values of the true
#' treatment effects, using simulation.
#'
#' @param des A \code{\link{list}} of class \code{"multiarm_des_gs_norm"}, as
#' returned by \code{\link{build_gs_norm}} or \code{\link{des_gs_norm}} (i.e., a
#' multi-stage group-sequential multi-arm clinical trial design for a normally
#' distributed outcome). \strong{Note:} The sample sizes in all arms must be
#' whole numbers. Defaults to \code{des_gs_norm(integer = TRUE)}.
#' @param tau A \code{\link{matrix}} whose rows indicate values of
#' \ifelse{html}{\out{<b><i>τ</i></b>}}{\eqn{\bold{\tau}}} at which to
#' evaluate the operating characteristics. Defaults internally to the global
#' null, global alternative, and each of the least favourable configurations if
#' unspecified.
#' @param replicates A \code{\link{numeric}} indicating the number of replicate
#' simulations to use for each value of
#' \ifelse{html}{\out{<b><i>τ</i></b>}}{\eqn{\bold{\tau}}}. Defaults to
#' \code{1e5}.
#' @param summary A \code{\link{logical}} variable indicating whether a summary
#' of the function's progress should be printed to the console. Defaults to
#' \code{FALSE}.
#' @return A \code{\link{list}} containing the following elements
#' \itemize{
#' \item A \code{\link{tibble}} in the slot \code{$sim} giving the estimated
#' operating characteristics.
#' \item Each of the input variables.
#' }
#' @examples
#' \dontrun{
#' # The estimated operating characteristics for the default parameters
#' sim <- sim_gs_norm()
#' }
#' @seealso \code{\link{build_gs_norm}}, \code{\link{des_gs_norm}},
#' \code{\link{opchar_gs_norm}}, \code{\link{plot.multiarm_des_gs_norm}}.
#' @export
sim_gs_norm <- function(des = des_gs_norm(integer = TRUE), tau,
replicates = 1e5, summary = FALSE) {
##### Check input variables ##################################################
#check_multiarm_des_gs_norm(des, T)
tau <- check_tau(tau, des)
replicates <- as.numeric(check_integer_range(replicates, "replicates",
c(0, Inf), 1))
check_logical(summary, "summary")
##### Print summary ##########################################################
if (summary) {
#summary_sim_gs(des, tau, replicates, "norm")
message("")
}
##### Perform main computations ##############################################
if (summary) {
message(" Beginning the required simulations..")
}
if (length(des$sigma) == 1) {
sigma <- rep(des$sigma, des$K + 1)
} else {
sigma <- c(des$sigma[1], rep(des$sigma[2], des$K))
}
nrow_tau <- nrow(tau)
total_replicates <- nrow_tau*replicates
sim <- matrix(0, nrow_tau, 4*des$K + 13)
for (i in 1:nrow_tau) {
sim[i, ] <-
sim_gs_norm_internal(tau[i, ], (i - 1)*replicates, des$n_factor, des$e,
des$f, sigma, des$ratio, des$spacing, des$stopping,
des$type, replicates, des$K, des$J, summary,
total_replicates)
}
seq_K <- 1:des$K
colnames(sim) <- c(paste0("tau", seq_K), "Pdis", "Pcon",
paste0("P", seq_K), paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER", "FDR", "pFDR", "FNDR",
"Sens", "Spec", "ESS", "SDSS", "MeSS", "MoSS", "maxN")
sim <- tibble::as_tibble(sim, .name_repair = "minimal")
if (summary) {
message("..completed the required simulations.")
message(" Preparing for outputting..")
}
##### Outputting #############################################################
if (summary) {
message("..outputting.")
}
list(des = des,
replicates = replicates,
sim = sim,
summary = summary,
tau = tau)
}
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