Abstract

Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and it might make it difficult to directly communicate results with decisions makers and other stakeholders. The R package Shiny has the potential to resolve this limitation. It allows programmers to embed health economic models developed in R into interactive web browser based user interfaces. Users can specify their own assumptions about model parameters and run different scenario analyses, which, in the case of regular a Markov model, can be computed within seconds. This paper provides a tutorial on how to wrap a health economic model built in R into a Shiny application. We use a four-state Markov model developed by the Decision Analysis in R for Technologies in Health (DARTH) group as a case-study to demonstrate main principles and basic functionality. A more extensive tutorial, all code, and data are provided in a GitHub repository.

Highlights

  • As the complexity of health economic decision models increase, there is growing recognition of the advantages of using high level programming languages (e.g. R, Python, C++, Julia) to support statistical analysis

  • This paper provides a tutorial on how to wrap a health economic model built in R into a Shiny application

  • While the focus of this tutorial is on the application of Shiny for health economic models, below we provide a brief overview of the “Sick-Sicker model”

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Summary

Introduction

As the complexity of health economic decision models increase, there is growing recognition of the advantages of using high level programming languages (e.g. R, Python, C++, Julia) to support statistical analysis. Certain types of models (e.g. individual-level simulations) can take a very long time to run or become computationally infeasible, and some essential statistical methods can hardly be implemented at all (e.g. survival modelling, network meta-analysis, value of sample information), or rely on exporting results from other programs (e.g. R, STATA, WinBUGs). Of all the high level programming languages, R is the most popular amongst health economists[1]. R is open source and supported by a large community of statisticians, data scientists and health economists. PubMed Abstract | Publisher Full Text | Free Full Text.

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