https://github.com/dark-peak-analytics/heconpack

Example R package for a health economic model

https://github.com/dark-peak-analytics/heconpack

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Example R package for a health economic model

https://github.com/dark-peak-analytics/HECONpack/blob/master/

# HECONpack 

This repository houses R package `HECONpack` created in the tutorial section of the working paper:

> Smith R, Mohammed W and Schneider P. Packaging cost-effectiveness models in R: A tutorial. version 1; peer review: awaiting peer review. Wellcome Open Res 2023, 8:419 [https://doi.org/10.12688/wellcomeopenres.19656.1](https://doi.org/10.12688/wellcomeopenres.19656.1)


# **Packaging cost-effectiveness models in R: A tutorial**

[Robert Smith](https://www.linkedin.com/in/robert-smith-53b28438)1,2, Wael Mohammed1,2 & Paul Schneider1,2

1 [University of Sheffield](https://www.sheffield.ac.uk/scharr), University of Sheffield, Sheffield, UK   
2 [Dark Peak Analytics](https://darkpeakanalytics.com/), Sheffield, UK

>#### **Background**
>
>The use of programming languages such as R in health economics and decision science is increasing, and brings numerous benefits including increas- ing model development efficiency, improving transparency, and reducing human error. However, there is limited guidance on how to best develop models using R. So far, no clear consensus has emerged.
>
>#### **Methods**
>
>We present the advantages of creating health economic models as R packages - structured collections of functions, data sets, tests, and documentation. Assuming an intermediate understanding of R, we provide a tutorial to demonstrate how to construct a basic R package for health economic evaluation. All source code used in or referenced by this paper is available under an open source licence.
>
>#### **Results**
>
>We use the Sick Sicker Model as a case study applying the steps from the tutorial to standardise model development, documentation and aid review. This can improve the distribution of code, thereby streamlining model development, and improve methods in health economic evaluation.
>
>#### **Conclusions**
>
>R Packages offer a valuable framework for enhancing the quality and transparency of health economic evaluation models. Embracing better, more standardised software development practices, while fostering a collaborative culture, has the potential to significantly improve the quality of health economic models, and, ultimately, support better decision making in healthcare.

## Installation

To test the functionality of this package, install the development version of the package using the devtools package.

``` r
devtools::install_github("dark-peak-analytics/HECONpack")
```

## Quick start

### Load the package.

``` r
library(HECONpack)
```

### Example function use

Use the `calcICER` function below to calculate the ICER given a set of expected outcomes.

``` r
calcICER(e_int = 28.3, e_base = 22.5, c_int = 10000, c_base = 9200)
```

## Cloning the repository

1. Make sure you have RStudio installed.
2. Clone this repository via the IDE or the command line with `git clone https://github.com/dark-peak-analytics/HECONpack.git`
3. Open `HECONpack.Rproj` in RStudio

### Contents

This repository is structured in the standard R Package structure.

- `.Rbuildignore` files to ignore when building Package.
- `.gitignore` files ignored by git.
- `DESCRIPTION` metadata, e.g. name and version.
- `NAMESPACE` from Roxygen, ensures names dependencies etc..
- `R/` R functions.
- `man/` md files documenting for functions.
- `data` data available within Package.
- `vignettes/` .Rmd files used to showcase Package functionality.
- `tests/` unit tests designed to ensure code works as intended.

For more guidance on Package setup more generally please refer to .

## Funding
Rob, Wael & Paul were joint funded by the Wellcome Trust Doctoral Training Centre in Public Health Economics and Decision Science [108903] and the University of Sheffield. They now all work for [Dark Peak Analytics](https://www.darkpeakanalytics.com). Please contact  with any queries.

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