Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


High-impact journals increasingly encourage that (some of) the data and the code used in clinical and policy research should be archived and made publicly available alongside a publication. Similarly, efforts are under way for making decision models, which form the basis of economic evaluations and cost-effectiveness analysis, publically
available, more transparent, reproducible, and adaptable to new applications. R is an open-source programming environment that can facilitate model transparency, reproducibility, and shareability, through a number of innovative ways (Markdown, Shiny etc.). However, realizing this potential can be challenging given the lack of
expertise and standardized use in the field of health decision analysis, and the famous “steep learning curve” of R. Over the past years, a number of groups around the globe have worked, in parallel, on the development of materials to conduct decision modelling in R, analyse the results of decision models, improve and harmonize the link between input parameters and decision models as well as standardize the use of R in decision analysis. During this seminar we will talk about the use of R in the field of health decision making, its advantages and disadvantages. We will talk about existing and future trends in the work of these groups and highlight some examples. Finally, we will briefly illustrate the use of R for decision analysis through the recently developed darthpack R package.