Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Eline M. Krijkamp, Fernando Alarid-Escudero, Eva A. Enns, Hawre J. Jalal, M. G. Myriam Hunink and Petros Pechlivanoglou
Additional contact information
Eline M. Krijkamp: Erasmus MC, Epidemiology Department, Rotterdam, The Netherlands
Fernando Alarid-Escudero: University of Minnesota School of Public Health, Minneapolis, MN, USA
Eva A. Enns: University of Minnesota School of Public Health, Minneapolis, MN, USA
Hawre J. Jalal: University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
M. G. Myriam Hunink: Erasmus MC, Epidemiology Department, Rotterdam, The Netherlands
Petros Pechlivanoglou: Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada

Medical Decision Making, 2018, vol. 38, issue 3, 400-422

Abstract: 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.

Keywords: decision-analytic modeling; Markov model; microsimulation; open source software; R project (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:3:p:400-422

DOI: 10.1177/0272989X18754513

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