OBJECTIVES: Cost-effectiveness analysis frequently leads to disputes in the scientific literature. The reason for these disputes often stem from disagreement or misunderstanding about the underlying model structure. At the same time, models quickly become outdated as the evidence base evolves. Our aim was to develop a transparent, flexible, and accessible cost-effectiveness model to help achieve consensus and ensure that estimates of cost-effectiveness reflect current evidence.

METHODS: We developed an R package to run an individual patient simulation (IPS) model for evaluating the cost-effectiveness of treatments for rheumatoid arthritis. The model allows for multiple perspectives (i.e., health care sector, societal) and 280 possible model structures related to the initial treatment effect, the relationship between the initial treatment effect and treatment switching, the survival distribution used to model time to discontinuation, and the algorithm used to estimate utility. The IPS is primarily written in C++ so that probabilistic sensitivity analysis and analyses of structural uncertainty can be run in a reasonable amount of time. We also created a user-friendly R Shiny web application where users can modify parameter values or structural assumptions and run the model online.

RESULTS: The R package, iviRA, is available on a public GitHub repository and the Shiny web application is freely available online at shinyapps.io. Documentation provides step-by-step instructions for conducting a cost-effectiveness analysis using the model with R. A description of the model is also available, which provides detailed information (i.e., mathematical formulas and algorithms) related to data sources, parameter estimation, and simulation techniques.

CONCLUSIONS: Transparent, flexible, and maintainable models can be developed in open-source programming languages such as R and C++. The models can be made accessible to non-modelers with user-friendly web applications, which, when combined with flexible models, can help resolve disputes by improving understanding of the reasons behind varying cost-effectiveness estimates.