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

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

## Overview

This tutorial walks through aggregate DCEA step-by-step for a hypothetical
NSCLC (lung cancer) treatment, following the Love-Koh et al. (2019) method.

## Step 1: Define CEA inputs

```{r inputs}
icer            <- 28000   # £/QALY
inc_qaly        <- 0.45    # incremental QALYs per patient
inc_cost        <- 12600   # incremental cost per patient (£)
population_size <- 12000   # eligible patients in England
wtp             <- 20000   # NICE standard WTP (£/QALY)
occ_threshold   <- 13000   # opportunity cost threshold (£/QALY)
```

## Step 2: Load baseline health distribution

```{r baseline}
baseline <- get_baseline_health("england", "imd_quintile")
baseline
```

## Step 3: Run aggregate DCEA

```{r run-dcea}
result <- run_aggregate_dcea(
  icer                       = icer,
  inc_qaly                   = inc_qaly,
  inc_cost                   = inc_cost,
  population_size            = population_size,
  disease_icd                = "C34",
  wtp                        = wtp,
  opportunity_cost_threshold = occ_threshold
)
```

## Step 4: Interpret outputs

```{r summary}
summary(result)
```

### Per-group results

```{r by-group}
result$by_group
```

### Inequality impact

```{r inequality}
result$inequality_impact
```

## Step 5: Visualise

```{r plane, fig.width = 6, fig.height = 5}
plot_equity_impact_plane(result)
```

```{r ede, fig.width = 6, fig.height = 4}
plot_ede_profile(result, eta_range = seq(0, 10, 0.2))
```

## Step 6: Generate NICE submission table

```{r nice-table}
generate_nice_table(result, format = "tibble")
```

## References

Love-Koh J et al. (2019). Value in Health 22(5): 518-526.
<https://doi.org/10.1016/j.jval.2018.10.007>