---
title: "Interactive Sensitivity Analysis"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
    number_sections: true
vignette: >
  %\VignetteIndexEntry{Interactive Sensitivity Analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

library(triangulate)
```

## Introduction

This vignette demonstrates how to use the `interactive_bias_plot()` function to explore how bias priors affect triangulated effect estimates in real time using a Shiny interface.

The function launches an interactive app with sliders that control the magnitude and uncertainty of additive and proportional bias. The adjusted effect estimates update automatically as the priors change.

## Example Data

We use a small example dataset with four hypothetical studies.

```{r}
example_data <- tibble::tibble(
  result_id = paste0("S", 1:4),
  study = paste("Study", 1:4),
  yi = c(-0.2, 0.25, -0.1, 0.15),
  vi = c(0.02, 0.03, 0.015, 0.025),
  type = "RCT"
)
```

## Launch Interactive App

To run the app, call the function below. This will open a Shiny window (locally) allowing you to interactively adjust the priors.

```{r eval=FALSE}
interactive_bias_plot(example_data)
```

> 📌 **Note**: This command must be run in an interactive R session (e.g., RStudio). It won’t execute inside a knitted document but will launch a Shiny app when run line-by-line.

## Conclusion

This tool is useful for exploring how robust your triangulated estimates are to different prior assumptions. You can use it with your own data by passing a data frame with columns:

- `result_id`
- `study`
- `type`
- `yi` (effect estimate)
- `vi` (variance)