This course will teach participants how to implement microsimulation models in R. We will first provide a conceptual overview of a microsimulation model and general structure for its implementation. This will be followed by a brief review of relevant R commands and concepts, including data structures, creating variables and functions, sampling random numbers, and basic numerical manipulations. We will then engage in hands-on programming exercises to implement a simple Markov cohort model as a microsimulation, followed by models of incrementally increasing complexity. By the end of the course, participants will have implemented microsimulation models with baseline patient heterogeneity, time-varying probabilities, and history-dependent probabilities. We will also cover methods for visualizing and analyzing the output of microsimulation models. Throughout the course, we will highlight good programming principles. We will conclude with a discussion and demonstration of the computational efficiency of different model implementations and highlight R functions related to parallel processing.

At the end of the course, participants will be able to:

• Construct microsimulation models in R with any of the following elements:
  • Population heterogeneity
  • Time-varying probabilities
  • History-dependent probabilities, costs, and/or utilities
• Visualize and analyze microsimulation outputs in R
• Understand computational efficiency considerations in implementing a microsimulation, including parallel processing functions in R
• Appreciate the advantages and challenges of using R in decision modeling

All R programming templates used in this short course will be provided to participants after the course for future use.