R-HTA is a consortium focused on popularising the use of R for health economics and health technology assessment (HTA). The main body of R-HTA includes members primarily from the Americas and UK, who have diverse experience in government (including NICE in the UK), academia, and industry. The aims of R-HTA, in general, are to:

  • Provide discussion on the many R packages available to HTA analysts;
  • Assist users to get the most out of R for cost-effectiveness analysis; and
  • Host presentations and public discussions to facilitate the development of R for HTA ‘R-HTA in LMICs’ is the LMIC-focused chapter of this consortium that aims to introduce and showcase the strengths of R specifically to LMIC analysts and health care institutions


The R for HTA in LMICs chapter is the LMIC-focused chapter of the R-HTA consortium, aiming to introduce and showcase the strengths of R specifically to LMIC analysts and health care institutions.

Why an LMIC chapter?

Microsoft Excel and other software such as TreeAge remain popular software for HTA modelling, especially in LMICs.

  • Many standard software used for HTA and health economics require costly subscriptions
  • R is a free, open source software - an obvious benefit in LMICs

Because LMICs are moving towards the principles of Universal Health Coverage (UHC) there will be an increased demand for complex HTA methodology, such as in oncology and other rare diseases.

  • This will make the need for more efficient and comprehensive software for HTA within LMICs gradually more necessary

There are also limited in-depth tutorials on R focused for LMIC HTA analysts.

  • Hence, there is a need to provide accessible workshops that showcase the strengths of R while also providing online coding resources for LMIC analysts.

The aim of the LMIC chapter is to thus bridge the gap in the use of R software among HTA analysts in LMICs. The chapter hosts both presentation-focused and tutorial-focused R workshops. For example, in introductory tutorial workshops, participants are introduced to the basics of R and several open-source HTA R packages; more advanced topics include developing R-HTA models in interactive formats, using Shiny (click here for details).

All attendees are provided with the resources and code used in every workshop, which can be found on the chapter’s Github.

Why R?

R scales efficiently

  • This is especially true when an HTA model becomes increasingly complex compared to other commonly used software (read more here)

R has an established user-base

  • There are several health economics packages which make coding in R more user-friendly: BCEA, hesim, heemod, and dampack

R ensures reproducibility

  • Models can easily and safely be hosted and shared via services such as Github
  • Using ‘version control’, updates do not ‘break’ models
  • With RMarkdown and Shiny, analysts can deliver transparent, reproducible reports and interactive models