In the first quarter of every year, the chapter hosts a presentation-based workshop where LMIC university students and members of partnership organisations present their R related health economics work. Please see the latest workshop programme below accompanied by links to each presenter’s materials.

2022 Programme

The programme for the inaugural 2022 first-quarter workshop is found below.

Note: the recording of presentations will be uploaded as soon as possible. We are currently in the process of editing the February workshop.

Introduction

Presenters: Co-chairs of the R-HTA in LMICs chapter

An introduction to the inaugural R-HTA in LMICs workshop. The co-chairs discuss the motivations behind the chapter and introduce the team.

Getting Started with Github and R

Presenter: Joshua Soboil

A brief overview and tutorial on using Github with R and Rstudio.

  • Follow the step-by-step text tutorial here

BCEA: making decision modelling in R more user-friendly

Presenter: Dr Nathan Green

An introduction to the BCEA package and a presentation on how it enables more user-friendly health economic decision modelling in R.

  • Explore the package on CRAN

The Potential of R Shiny User Interfaces to Support Health Economic Decision Making

Presenters: Rose Hart and Robert Smith

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.

Explore the open source code on Github

Economic Burden of Female Genital Mutilation in 27 High-Prevalence Countries: a series of national cohort models in R and a web app in Shiny

Presenter: David Tordup

An overview of a model on FGM commissioned by the World Health Organization, in collaboration with Triangulate Health Ltd, presented in an interactive format using Shiny.

Read more on the study in BMJ Global Health

A Simplified Model of the Cost-Effectiveness of Screening: an open-source teaching and research tool coded in R

Presenter: Yi-Shu Lin

Models applied in cost-effectiveness analyses of screening are typically designed to address specific policy questions and consequently tend to be large and complex. They are not well suited to teaching the fundamentals of screening modelling or to demonstrating novel modelling methods.

The presentation thus discusses a lightweight, fully shareable and transparent screening model for teaching and methods research. This is a simplified, discrete-event, microsimulation model of screening coded in R and supported with an Excel-based user interface for the specification of input parameters. The model's components relating to the natural history of disease, test performance and anticipated health gain and healthcare costs are also demonstrated.

Access the model code on Githib and view the presentation here.

Budget Impact Analysis: a Shiny based calculator

Presenter: Dr Federico Cairoli

A Budget Impact Analysis presentation focusing Shiny and its ability to provide decision makers with an interactive model format.

Explore the Shiny App and use the code ‘IECS2024’ when prompted.

Modelling COVID in R: an experience in using R over Excel for SIR modelling

Presenter: Dr Ivan Zimmerman

A presentation on the experience of moving from Excel to R for SIR modelling. The presentation discusses why modelling an SIR model in Excel takes considerable time and has a high probability of error, due to cell replication. In comparison, using R is much simpler and less prone to error.

The code for this model is not open-source

The Cost-Effectiveness of COVID Vaccines: a Shiny presentation

Presenter: Dr Alejandro Lopez Osornio

A an interactive presentation on the health impact and cost-effectiveness of COVID-19 vaccines for 26 Latin American counties, using Shiny.

Explore the Shiny App and open source code on Github

A Need for Change! A Coding Framework for Improving Transparency in Decision Modelling

Presenter: Dr Fernando Escudero

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and share-ability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modellers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, the presentation provides an overview of a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R.

Read the open-access article here

Panel discussion: are the features of modern software tools ( R ) also useful for HTA modeling in LMICs?

Panelists: Assistant Prof Fernando Alarid-Escudero, Prof Gianluca Biao, Dr Lucy Cunnama, and Dr Howard Thom

Moderator: Buhle Ndweni

The panel discussion centred on the potential strengths and weaknesses of R for HTA in LMICs across four domains: clinical realism, quantifying decision uncertainty, transparency/reproducibility, and reusability/adaptability.