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 and suggest strategies for debugging errors. 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.