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.
FriedmanSG. Current management of the patient with internal carotid artery occlusion. Eur J Vasc Surg. 1989;3:97–101.
2.
KattanM. Encyclopedia of Medical Decision Making. California: SAGE Publications, Inc.; 2009. doi:10.4135/97814 12971980.
3.
KrekeJESchaeferAJRobertsMS. Simulation and critical care modeling. Current Opin Crit Care. 2004;10:395–8.
4.
RobertsMRussellLBPaltielAD. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2. Value Health. 2012;15:804–11.
5.
CaroJJBriggsAHSiebertUKuntzKM, ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Value Health. 2012;15:796–803.
HuninkMGMWeinsteinMCWittenbergE. Decision Making in Health and Medicine. Integrating Evidence and Values. 2nd ed.Cambridge: Cambridge University Press; 2014.
8.
Lay-YeeRCotterellG. The role of microsimulation in the development of public policy;2015. doi:10.1007/978-3-319-12784-2_14.
9.
SiebertUAlagozOBayoumiAM. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Med Decis Making. 2012;32:690–700.
10.
ZucchelliEJonesAMRiceN. The evaluation of health policies through microsimulation methods. Health, Econometrics and Data Group (HEDG) Working Papers. 2010;5: 2–20. Available from: http://ideas.repec.org/p/yor/hectdg/10-03.html.
11.
BrennanAChickSEDaviesR. A taxonomy of model structures for economic evaluation of health technologies. Health Econ. 2006;15:1295–310.
WolfsonMC. POHEM–a framework for understanding and modelling the health of human populations. World Health Stat Qrtly. 1994;47(3-4):157–76.
14.
HennessyDAFlanaganWMTanuseputroP. The Population Health Model (POHEM): an overview of rationale, methods and applications. Pop Health Metrics. 2015;13:24.
MuhlbergerNKurzthalerCIskandarR. The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening. Med Decis Making. 2015;35(August):758-772.
18.
HollmanCPauldenMPechlivanoglouPMcCabeC. A comparison of four software programs for implementing decision analytic cost effectiveness models. PharmacoEconomics. 2017.
19.
JalalHPechlivanoglouPKrijkampEAlarid-EscuderoFEnnsEHuninkMGM. An overview of R in health decision sciences. Med Decis Making. 2017;37:735–46.
20.
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2015. Available from: http://www.R-project.org/.
BaioG. Bayesian Methods in Health Economics. 1st ed. London: Chapman and Hall/CRC; 2012.
24.
WilliamsCLewseyJDBriggsAHMackayDF. Cost-effectiveness analysis in R using a multi-state modeling survival analysis framework: A tutorial. Med Decis Making. 2017;37:340–52.
25.
WeeksMMittonLSutherlandH. Microsimulation Modelling for Policy Analysis Challenges and Innovations. Cambridge: Cambridge University Press; 2000 p 1–11.
SawilowskySSFahoomeGC. Statistics via Monte Carlo Simulation with Fortran. Rochester Hills, MI: JMASM; 2003.
28.
KurtzTG. Limit Theorems for sequences of jump Markov processes approximating ordinary fifferential processes. J Appl Probab. 1971;8:344–56.
29.
WhittW. Stochastic-Process limits: An introduction to stochastic-process limits and their application to queues. New York, NY: Springer; 2002. Available from: http://www.columbia.edu/~ww2040/preflongno.pdf.
30.
TezukaS. Uniform Random Numbers: Theory and Practice. Boston, MA: Springer US; 1995. doi:10.1007/978-1-4615-2317-8.
31.
EnnsEACiprianoLESimonsCTKongCY. Identifying best-fitting inputs in health-economic model calibration: A Pareto frontier approach. Med Decis Making. 2015;35:170–82.
RobertsMRussellLBPaltielAD. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Med Decis Making. 2012;32:678–89.
MancaAHawkinsNSculpherMJ. Estimating mean QALYs in trial-based cost-effectiveness analysis: The importance of controlling for baseline utility. Health Econ. 2005;14:487–96.
36.
ChoiSEBrandeauMLBasuS. Expansion of the National Salt Reduction Initiative: A mathematical model of benefits and risks of population-level sodium reduction. Med Decis Making. 2015;36:1–14.
BirnbaumJKAdemuyiwaFOCarlsonJJMallingerLMasonMWEtzioniR. Comparative effectiveness of biomarkers to target cancer treatment: Modeling implications for survival and costs. Med Decis Making. 2015:1–10.
39.
EddelbuettelDFrançoisR. Rcpp: Seamless R and C++ Integration. J Stat Software. 2011;40:1–18.
40.
GillespieCLovelaceR. Efficient R Programming: A Practical Guide to Smarter Programming. 1st ed. O’Reilly Media; 2016.
41.
PetzoldtTRinkeK. simecol: An object-oriented framework for ecological modeling in R. J Stat Software. 2007;22:1–31.
BriggsAHWeinsteinMCFenwickEAL. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Making. 2012;32:722–32.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
