#' Decision Tree for ACE project
#'
#' Calculate decision tree expected costs and QALY loss
#' for N simulations
#'
#' @param parameters
#' @param N.mc Number of Monte Carlo iterations
#' @param cost_dectree Rds file names with extension
#' @param health_dectree Rds file names with extension
#'
#' @return list
#' @export
#'
#' @examples
#'
decision_tree_ACE <- function(parameters,
N.mc = 2,
cost_dectree = "osNode_cost.Rds",
health_dectree = "osNode_health.Rds",
LTBI_pathString,
N = 100){
mcall <- match.call()
osNode.cost <- readRDS(file = paste0("data/", cost_dectree))
osNode.health <- readRDS(file = paste0("data/", health_dectree))
assign_branch_values(osNode.cost,
osNode.health,
parameter_p = subset(parameters, val_type == "QALYloss"),
parameter_cost = subset(parameters, val_type == "cost"))
LTBI_Dx_prob <- total_pathway_prob_sample(osNode.cost,
pathString = LTBI_pathString,
N.mc)
LTBI_Dx_ssize <- patient_level_ssize_sample(N = N,
osNode = osNode.cost,
pathString = LTBI_pathString,
N.mc = N.mc)
mc_cost <- MonteCarlo_expectedValues(osNode = osNode.cost,
n = N.mc)
mc_health <- MonteCarlo_expectedValues(osNode = osNode.health,
n = N.mc)
list(osNode_cost = osNode.cost,
osNode_health = osNode.health,
mc_cost = as.numeric(mc_cost$`expected values`),
mc_health = as.numeric(mc_health$`expected values`),
LTBI_Dx_prob = LTBI_Dx_prob,
LTBI_Dx_ssize = LTBI_Dx_ssize,
# call = mcall,
N = N,
N.mc = N.mc)
}
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