Historically, economic models for cost-effectiveness analyses have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). But more recently there has been increasing interest in using R and other programming languages for cost-effectiveness analysis, which can offer advantages regarding the integration of input parameter estimation and model simulation, the evaluation of structural uncertainty, quantification of decision uncertainty, incorporation of patient heterogeneity, and computational efficiency, among others. Programming languages such as R also facilitate reproducibility of model-based cost-effectiveness analysis, which is more relevant than ever given recent calls for increased transparency. While these tools are still relatively new, there is an increased interest in learning opportunities as evidenced by recent tutorials, workshops, and development of open-source software.
The course consists of different modules consisting of short slide presentations explaining economic modeling topics followed by applied examples programmed using R. Participants will be asked to modify the models (e.g. adding health states, use of alternative time-to-event distributions) and run analyses (e.g. cost-effectiveness analysis, probabilistic sensitivity analysis, evaluating structural uncertainty, and value of information analysis). The course has been designed to be provided online. All participants will have access to a GitHub repository and website prior to the course that will contain (i) R code to run the economic models, and (ii) R Markdown files to explain and reproduce the analyses covered in the course. Participants will work through examples on a cloud based server with R, RStudio, and required packages preinstalled.
After completion of the course, participants will be able to:
Devin Incerti, PhD
Genentech
Jeroen Jansen, PhD