Discrete Event Simulation in R to Support Healthcare Decision Making

Description and Objectives:

Discrete event simulations (DES) are able to represent the complex dynamics of today’s clinical practice, but are often perceived as complex compared with Markov cohort and micro simulation models. Consequently, DES may be avoided by novice modelers even in contexts where using DES would have a clear advantage over more traditional modeling methods. To contribute to a new generation of modelers familiar with the flexibility offered by DES, this course will cover all the basics up to developing and analyzing DES models. Using ISPOR-SMDM Modeling Taskforce Reports as a starting point, this course will discuss the practical steps involved in the modeling process. A single case study will be used throughout several hands-on exercises, in which a simple DES will be extended into a comprehensive health economic model, and probabilistic analysis is performed. R will be used for all exercises. There will be a brief Q&A at the course end to plenary discuss participants’ questions on advanced DES methods and applications. Participants will leave the course with a running DES and equipped with the knowledge and tools required to develop DES models for their own projects.

After the completion of this course, participants will be able to:

  • Understand the design choices necessary for developing DES.

  • Correctly utilize different types of evidence to populate DES.

  • Distinguish between alternative approaches for implementing competing events in DES.

  • Distinguish between different types of uncertainty in DES.

  • Develop and run a basic DES in R using the simmer package.

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