Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.
Bibliographical noteFunding Information:
Erasmus MC, Epidemiology Department, Rotterdam, The Netherlands (EMK, MGMH); University of Minnesota School of Public Health, Minneapolis, MN, USA (FAE, EAE); University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA (HJJ); Erasmus MC, Radiology Department, Rotterdam, The Netherlands (MGMH) Harvard T.H. Chan School of Public Health, Center for Health Decision Science, Boston, USA (MGMH); Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada (PP); Institute of Health Policy Management and Evaluation, University of Toronto, ON, Canada (PP). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Financial support for this study was provided by the Doctoral Dissertation Fellowship from the Graduate School of the University of Minnesota (FAE); and a grant from National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award no. K25AI118476) (EAE). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Royalties are received for the textbook ‘Decision Making in Health and Medicine: Integrating Evidence and Values’ (MGMH). There are no other conflicts of interest to disclose.
© 2018, © The Author(s) 2018.
- Markov model
- R project
- decision-analytic modeling
- open source software