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This guide demonstrates building discrete event simulation (DES) models in R using the Scottish Cardiovascular Disease (CVD) Policy Model. It helps health economists transition from Markov models to DES for more accurate evaluations.

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Area of Science:

  • Health economics
  • Computational modeling
  • Biostatistics

Background:

  • The Scottish Cardiovascular Disease (CVD) Policy Model, originally a complex Excel-based Markov model, has been replicated in R.
  • Transitioning from established Markov models to Discrete Event Simulation (DES) presents challenges for health economic modelers.
  • Publicly available R code for complex models offers opportunities for practical DES implementation training.

Purpose of the Study:

  • To provide a step-by-step guide for building Discrete Event Simulation (DES) models in R.
  • To illustrate DES techniques using the Scottish Cardiovascular Disease (CVD) Policy Model as a case study.
  • To equip practitioners with the skills to transition from Markov to DES frameworks in health economic evaluations.

Main Methods:

  • Utilizing base R functions and the tidyverse package for transparent and reproducible DES model construction.
  • Demonstrating simulation of time-to-event data based on specified distributions.
  • Implementing continuous discounting and addressing advanced challenges like piecewise cost and utility modeling.

Main Results:

  • Successfully adapted the intricate Scottish CVD Policy Model structure into a DES framework in R.
  • Provided clear, reproducible methods for fundamental and advanced DES implementation without specialized DES packages.
  • Showcased the utility of DES in handling complex health economic modeling scenarios.

Conclusions:

  • Discrete Event Simulation (DES) offers enhanced accuracy and flexibility for health economic evaluations compared to traditional Markov models.
  • This R-based illustration provides a practical pathway for modelers to adopt DES techniques.
  • The methodology facilitates a deeper understanding and application of DES in public health policy modeling.