R/data_model_output_sick_sicker.R

In tatooheene: Technology Appraisal Toolbox for Health Economic Evaluations in the Netherlands

#' Example model output for the sick-sicker model
#'
#' This data structure with several lists and vectors collecting a subset of the model outputs from the Sick-Sicker model intended for demonstrating the discounting in this package. The full model is described in the DARTH “Sick-Sicker” example.
#'Source code can be found here: https://github.com/DARTH-git/cohort-modeling-tutorial-intro
#'
#' For the full reference read: Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example. Medical Decision Making, 2023;43(1):3-20. https://doi.org/10.1177/0272989X221103163
#'
#' These data can be used for demonstrating how to apply some of the package functions like discounting for the results of a model-based cost-effectiveness model.
#'
#' #' @format A list with the following components
#' \describe{
#'   \item{l_m_M_annual}{Annual transition matrix for sick-sicker model.}
#'   \item{l_m_M_monthly}{Monthly transition matrix for sick-sicker model.}
#'   \item{v_wcc_annual}{Half-cycle correction weights for annual model.}
#'   \item{v_wcc_monthly}{Half-cycle correction weights for monthly model.}
#'   \item{v_names_str}{Names of the intervention strategies used in the model.}
#'   \item{l_u_annual}{Annual utilities for each health state.}
#'   \item{l_u_monthly}{Monthly utilities for each health state.}
#'   \item{l_c_annual}{Annual costs for each health state.}
#'   \item{l_c_monthly}{Monthly costs for each health state.}
#' }
#'

#' @examples
#' # Load the dataset
#' data("data_model_output_sick_sicker.rda")
#'
#'# Load list in global environment
#' list2env(data_model_output_sick_sicker, envir = .GlobalEnv)
#' # Explore the available objects
#' ls(pattern = "l_|v_")
#'
#' # View names of health states
#' v_names_str
#'
#' # Inspect the first few cycles of the annual Markov trace for the first strategy
#' head(l_m_M_annual[[1]])
#'
#' # Compare dimensions of annual and monthly traces
#' dim(l_m_M_annual[[1]])
#' dim(l_m_M_monthly[[1]])
#'
#' # Apply the half cycle correction
#' ## Loop through each strategy and calculate total utilities and costs ----
#'
#' v_tot_qaly <- v_tot_cost <- c()
#'for (i in 1:length(v_names_str)) {
#'  v_u_str <- l_u_monthly[[i]]   # select the vector of state utilities for the i-th strategy
#'  v_c_str <- l_c_monthly[[i]]   # select the vector of state costs for the i-th strategy
#'
#'   ###* Expected QALYs and costs per cycle
#'   ##* Vector of QALYs and Costs
#'   #* Apply state rewards
#'   v_qaly_str <- l_m_M_monthly[[i]] %*% v_u_str # sum the utilities of all states for each cycle
#'   v_cost_str <- l_m_M_monthly[[i]] %*% v_c_str # sum the costs of all states for each cycle
#'
#'   ###*  Total expected QALYs and costs per strategy and apply half-cycle correction (if applicable)
#'   #* QALYs
#'   v_tot_qaly[i] <- t(v_qaly_str) %*% v_wcc_monthly
#'   #* Costs
#'   v_tot_cost[i] <- t(v_cost_str) %*% v_wcc_monthly
#' }

"data_model_output_sick_sicker"