---
title: "International Applications of DCEA"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{International Applications of DCEA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(dceasimR)
```

## Canada (CADTH workflow)

CADTH uses income quintile stratification. Use `country = "canada"` to
access the preloaded Statistics Canada HALE data.

```{r canada}
canada_baseline <- get_baseline_health("canada", "income_quintile")
canada_baseline

result_ca <- run_aggregate_dcea(
  icer                       = 50000,   # CAD/QALY
  inc_qaly                   = 0.40,
  inc_cost                   = 20000,
  population_size            = 8000,
  baseline_health            = canada_baseline,
  wtp                        = 50000,
  opportunity_cost_threshold = 30000
)
summary(result_ca)
```

## WHO regional analysis

For global health or multi-country analyses, use WHO regional HALE data.

```{r who}
who_baseline <- get_baseline_health("who_regions")
who_baseline
```

```{r who-dcea, fig.width = 6, fig.height = 5}
result_who <- run_aggregate_dcea(
  icer                       = 1000,
  inc_qaly                   = 0.35,
  inc_cost                   = 350,
  population_size            = 500000,
  baseline_health            = who_baseline,
  wtp                        = 1000,
  opportunity_cost_threshold = 600
)
plot_equity_impact_plane(result_who)
```

## Custom country workflow

For countries without preloaded data, supply your own baseline:

```{r custom}
custom_baseline <- tibble::tibble(
  group           = 1:4,
  group_label     = c("Poorest quartile", "Q2", "Q3", "Richest quartile"),
  mean_hale       = c(55.0, 60.0, 65.0, 70.0),
  se_hale         = c(0.8, 0.7, 0.6, 0.5),
  pop_share       = rep(0.25, 4),
  cumulative_rank = c(0.125, 0.375, 0.625, 0.875),
  year            = 2022L,
  source          = "Custom country data"
)

result_custom <- run_aggregate_dcea(
  icer                       = 5000,
  inc_qaly                   = 0.3,
  inc_cost                   = 1500,
  population_size            = 100000,
  baseline_health            = custom_baseline,
  wtp                        = 5000,
  opportunity_cost_threshold = 3000
)
summary(result_custom)
```