Review of open libraries for pharmacoeconomic analysis in R environment

Background. To reveal the full complexity of the relationship between medical intervention and disease outcome, new methods of analysis and modeling are actively being developed, and tools are becoming more complex, for the use of which it is important to understand their limitations and advantages. Objective: conducting a critical review of the main open-source packages in R environment for conducting pharmacoeconomic analysis. Material and methods. The selection of libraries used for pharmacoeconomic analysis in the R environment was carried out based on the keywords “health economic”, “DALY”, “QALY” in the CRAN repository. Only libraries that were valid on the date of the review were included in the study. The selected 10 R software libraries for pharmacoeconomic analysis were reviewed from the standpoint of the number of tools they support, the format of the data used, the possibilities of visualizing results and generating reports, the presence of vignettes and the possibilities of parallelizing calculations. Results. The selected libraries can be divided into three classes: packages for calculating various quality of life indices, libraries for calculating indicators and indices of economic effectiveness of medical interventions (DALY, QALY, ICER), libraries for performing sensitivity analysis of the effect of medical interventions based on decision tree algorithms and Markov models. The libraries “heemod”, “hesim”, “rdesign” allow building simple Markov and semi-Markov models, but preference should be given to “heemod” due to the presence of vignettes. To conduct an analysis using cohort Markov models, partitioned survival models, it is recommended to use the “hesim” library, if there are gaps in the results, it is recommended to use “missingHE”. The “rdesigin” library allows building decision trees indicating the risk of developing certain conditions and the cost of therapy. The “survHE” library for survival analysis, used specifically in health economics, allows you to carry out probabilistic sensitivity analysis based on survival models. To calculate the survival models themselves to identify predictors of a patient's transition from one health state to another, you will need to additionally install the “flexsurv” library. To visualize the results of pharmacoeconomic modeling, you should additionally install the “diagram” and “ggplot2” libraries. Conclusion. The conducted critical review of open source libraries in R environment can serve as a navigator for choosing a tool for performing pharmacoeconomic analysis.