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#' @name multi.ce
#' @title Cost-effectiveness Analysis With Multiple Comparison
#'
#' @description Computes and plots the probability that each of the `n_int` interventions
#' being analysed is the most cost-effective and the cost-effectiveness
#' acceptability frontier.
#'
#' @template args-he
#'
#' @return Original `bcea` object (list) of class "pairwise" with additional:
#' \item{p_best_interv}{A matrix including the probability that each
#' intervention is the most cost-effective for all values of the willingness to
#' pay parameter}
#' \item{ceaf}{A vector containing the cost-effectiveness acceptability frontier}
#'
#' @author Gianluca Baio
#' @seealso [bcea()],
#' [ceaf.plot()]
#' @keywords hplot dplot
#'
#' @examples
#' # See Baio G., Dawid A.P. (2011) for a detailed description of the
#' # Bayesian model and economic problem
#'
#' # Load the processed results of the MCMC simulation model
#' data(Vaccine)
#'
#' # Runs the health economic evaluation using BCEA
#'
#' m <- bcea(e=eff, c=cost, # defines the variables of
#' # effectiveness and cost
#' ref=2, # selects the 2nd row of (e,c)
#' # as containing the reference intervention
#' interventions=treats, # defines the labels to be associated
#' # with each intervention
#' Kmax=50000, # maximum value possible for the willingness
#' # to pay threshold; implies that k is chosen
#' # in a grid from the interval (0,Kmax)
#' plot=FALSE # inhibits graphical output
#' )
#'
#' mce <- multi.ce(m) # uses the results of the economic analysis
#'
#' ceac.plot(mce)
#' ceaf.plot(mce)
#'
#' @export
#'
multi.ce.bcea <- function(he) {
p_best_interv <- compute_p_best_interv(he)
ceaf <- compute_ceaf(p_best_interv)
res <- c(he,
list(p_best_interv = p_best_interv,
ceaf = ceaf))
structure(res, class = c("pairwise", class(he)))
}
#' @export
#'
multi.ce <- function(he) {
UseMethod('multi.ce', he)
}
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