#' Summary Methods For Objects in the Class `mixedAn` (Mixed Analysis)
#'
#' Prints a summary table for the results of the mixed analysis for the
#' economic evaluation of a given model.
#'
#' @param object An object of the class `mixedAn`, which is the results of
#' the function [mixedAn()], generating the economic evaluation of a
#' set of interventions, considering given market shares for each option.
#' @param wtp The value of the willingness to pay chosen to present the analysis.
#' @param ... Additional arguments affecting the summary produced.
#' @return Produces a table with summary information on the loss in expected
#' value of information generated by the inclusion of non cost-effective
#' interventions in the market.
#' @author Gianluca Baio
#' @seealso [bcea()],
#' [mixedAn()]
#' @importFrom Rdpack reprompt
#'
#' @references
#'
#' \insertRef{Baio2009}{BCEA}
#'
#' \insertRef{Baio2011}{BCEA}
#'
#' \insertRef{Baio2013}{BCEA}
#'
#' @keywords print
#'
#' @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)
#' )
#'
#' mixedAn(m) <- NULL # uses the results of the mixed strategy
#' # analysis (a "mixedAn" object)
#' # the vector of market shares can be defined
#' # externally. If NULL, then each of the T
#' # interventions will have 1/T market share
#'
#' # Prints a summary of the results
#' summary(m, # uses the results of the mixed strategy analysis
#' wtp=25000) # (a "mixedAn" object)
#' # selects the relevant willingness to pay
#' # (default: 25,000)
#'
#' @export
#'
summary.mixedAn <- function(object,
wtp = 25000,...) {
if (max(object$k) < wtp) {
wtp <- max(object$k)
}
if (length(which(object$k == wtp)) == 0) {
stop(
paste0(
"The willingness to pay parameter is defined in the interval [0-",
object$Kmax,
"],
with increments of ",
object$step,
"\n")
)
}
n.digits <- 2
n.small <- 2
cat("\n")
cat(paste0(
"Analysis of mixed strategy for willingness to pay parameter k = ",
wtp, "\n"))
cat("\n")
cat(
paste0(
"Reference intervention: ",
object$interventions[object$ref],
" (",
format(100 * object$mkt.shares[object$ref],
digits = n.digits,
nsmall = n.small),
"% market share)\n")
)
if (object$n_comparisons == 1) {
text.temp <-
paste0(
"Comparator intervention: ",
object$interventions[object$comp],
" (",
format(
100 * object$mkt.shares[object$comp],
digits = n.digits,
nsmall = n.small),
"% market share)\n")
cat(text.temp)
}
if (object$n_comparisons > 1) {
text.temp <-
paste0(
"Comparator intervention(s): ",
object$interventions[object$comp[1]],
" (",
format(
100 * object$mkt.shares[object$comp[1]],
digits = n.digits,
nsmall = n.small),
"% market share)\n")
cat(text.temp)
for (i in 2:object$n_comparisons) {
cat(paste0(
" : ",
object$interventions[object$comp[i]],
" (",
format(
100 * object$mkt.shares[object$comp[i]],
digits = n.digits,
nsmall = n.small),
"% market share)\n"))
}
}
cat("\n")
cat(paste0(
"Loss in the expected value of information = ",
format(
object$evi.star[object$k == wtp] - object$evi[object$k == wtp],
digits = n.digits,
nsmall = n.small),
"\n"))
cat("\n")
}
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