After following this course the student is able to
- Develop their own advanced, reproducible, discrete event simulation model in R using available empirical data
- Interpret the results obtained from a patient-level discrete event simulation model in a health and economic context, and communicate these results to peers and a broader lay audience;
- Understand the value and use of probability distributions, methods for uncertainty analysis, and metamodeling approaches in the context of discrete event simulation modeling
- Apply open source modeling in the context of health economic modeling
In many commercial software environments available for discrete event simulation (DES) the modeler is not in full control, and the environments may impose certain limitations on DES development, analysis, and use. Therefore, the interest in developing DES models in suitable open source programming environments, such as R, is rapidly increasing. Making use of special-purpose R packages, such as Simmer, developing DES models from scratch and performing any number of advanced (statistical) analyses with these models becomes straightforward and rewarding.|
This course is aimed at developing advanced discrete event simulation models in R to accurately reflect processes and events at the level of individuals (for example, citizens, patients, professionals etc) based on empirical data. Models are developed within a health economic case study, in which the impact of health innovations on health outcomes and costs needs to be quantified. However, the skills learned and experienced obtained from this course are directly generalizable to many other quantitative research fields.