---
title: "Generate a Static AE-Specific Table in GT format"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Generate a Static AE-Specific Table in GT format}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  out.width = "100%",
  dpi = 150
)
```

```{r}
library(metalite.ae)
```

## Overview

This vignette demonstrates how to generate a static AE-specific table 
reporting patients with **drug-related adverse events** by treatment group in a **gt** format.

The workflow uses three functions from
[metalite.ae](https://merck.github.io/metalite.ae/):

- `prepare_ae_specific()` prepares the analysis datasets.
- `format_ae_specific()` formats the results for reporting.
- `gt_ae_specific()` creates the RTF table.

Related vignettes explain how to
[customize displayed columns](ae-specific-custom-columns.html) and
[filter or sort rows](ae-specific-filter-sort.html). This guide also covers
basic gt customization and mock output.

## Procedure to generate an AE specific table in gt format

The example uses ADSL and ADAE data from the
[forestly](https://merck.github.io/forestly/) package.

### Step 1: Define metadata

```{r}
# Define metadata
adsl <- forestly::forestly_adsl
adae <- forestly::forestly_adae

adsl$TRT01A <- factor(
  adsl$TRT01A,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)
adae$TRTA <- factor(
  adae$TRTA,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)

analysis_plan <- metalite::plan(
  analysis = "ae_specific",
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

meta <- metalite::meta_adam(observation = adae, population = adsl) |>
  metalite::define_plan(analysis_plan) |>
  metalite::define_population(
    name = "apat",
    var = c(
      "USUBJID", "SAFFL", "TRT01A", "TRTDUR",
      "SITEID", "SEX", "RACE", "AGE"
    ),
    group = "TRT01A",
    subset = SAFFL == "Y",
    label = "All Participants as Treated"
  ) |>
  metalite::define_observation(
    name = "wk12",
    var = c(
      "USUBJID", "SAFFL", "TRTA", "AEDECOD", "AEBODSYS", "AEREL",
      "AESER", "AEOUT", "AEACN", "AESDTH", "ASTDT", "AENDT"
    ),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "Weeks 0 to 12"
  ) |>
  metalite::define_parameter(
    name = "rel",
    term1 = "Drug-Related",
    term2 = "",
    subset = AEREL %in% c("POSSIBLE", "PROBABLE"),
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Drug-related AEs"
  ) |>
  metalite::define_analysis(
    name = "ae_specific",
    title = "Participants with Drug-Related Adverse Events"
  ) |>
  metalite::meta_build()
```

<details>
<summary>Click to show the output</summary>
```{r}
meta
```
</details>

### Step 2: Generate the AE specific table

`prepare_ae_specific()` uses the population, observation, and parameter
definitions in `meta` to calculate the AE-specific analysis results. It returns
an `outdata` object for formatting and reporting.

```{r, message = FALSE}
prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  format_ae_specific() |>
  gt_ae_specific(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific"
  )
```
