IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v32y2012i5p701-711.html
   My bibliography  Save this article

Modeling Using Discrete Event Simulation

Author

Listed:
  • Jonathan Karnon
  • James Stahl
  • Alan Brennan
  • J. Jaime Caro
  • Javier Mar
  • Jörgen Möller

Abstract

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article is to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as the wider modeling task force.

Suggested Citation

  • Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    • Handle: RePEc:sae:medema:v:32:y:2012:i:5:p:701-711
    DOI: 10.1177/0272989X12455462
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X12455462
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X12455462?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    3. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefan Scholz & Thomas Mittendorf, 2014. "Modeling rheumatoid arthritis using different techniques - a review of model construction and results," Health Economics Review, Springer, vol. 4(1), pages 1-16, December.
    2. Xiuxian Wang & Na Geng & Jianxin Qiu & Zhibin Jiang & Liping Zhou, 2020. "Markov model and meta-heuristics combined method for cost-effectiveness analysis," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 213-235, March.
    3. Louise Freebairn & Jo-An Atkinson & Nathaniel D Osgood & Paul M Kelly & Geoff McDonnell & Lucie Rychetnik, 2019. "Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-27, June.
    4. Simone A. Huygens & Isaac Corro Ramos & Carlijn V. C. Bouten & Jolanda Kluin & Shih Ting Chiu & Gary L. Grunkemeier & Johanna J. M. Takkenberg & Maureen P. M. H. Rutten-van Mölken, 2020. "Early cost-utility analysis of tissue-engineered heart valves compared to bioprostheses in the aortic position in elderly patients," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(4), pages 557-572, June.
    5. Alison Ritter & Nagesh Shukla & Marian Shanahan & Phuong Van Hoang & Vu Lam Cao & Pascal Perez & Michael Farrell, 2016. "Building a Microsimulation Model of Heroin Use Careers in Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 140-176.
    6. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.
    7. Glasgow, Simon M. & Perkins, Zane B. & Tai, Nigel R.M. & Brohi, Karim & Vasilakis, Christos, 2018. "Development of a discrete event simulation model for evaluating strategies of red blood cell provision following mass casualty events," European Journal of Operational Research, Elsevier, vol. 270(1), pages 362-374.
    8. David Byrd & Sruthi Palaparthi & Maria Hybinette & Tucker Hybinette Balch, 2020. "The Importance of Low Latency to Order Book Imbalance Trading Strategies," Papers 2006.08682, arXiv.org.
    9. Matthew R Behrend & María-Gloria Basáñez & Jonathan I D Hamley & Travis C Porco & Wilma A Stolk & Martin Walker & Sake J de Vlas & for the NTD Modelling Consortium, 2020. "Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(4), pages 1-17, April.
    10. Alice Rondini & Fabiana Tornese & Maria Grazia Gnoni & Giuditta Pezzotta & Roberto Pinto, 2017. "Hybrid simulation modelling as a supporting tool for sustainable product service systems: a critical analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(23), pages 6932-6945, December.
    11. Miguel Ortiz-Barrios & Juan-José Alfaro-Saiz, 2020. "An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    12. Vojtěch Kamenský & Vladimír Rogalewicz & Ondřej Gajdoš & Gleb Donin, 2022. "Discrete Event Simulation Model for Cost-Effectiveness Evaluation of Screening for Asymptomatic Patients with Lower Extremity Arterial Disease," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    13. David Epstein & Leticia García-Mochón & Stephen Kaptoge & Simon G. Thompson, 2016. "Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 1041-1053, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    2. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    3. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.
    4. Marta O. Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    5. V. J. Roelofs & M. C. Kennedy, 2011. "Sensitivity Analysis and Estimation of Extreme Tail Behavior in Two‐Dimensional Monte Carlo Simulation," Risk Analysis, John Wiley & Sons, vol. 31(10), pages 1597-1609, October.
    6. Helen A. Dakin & José Leal & Andrew Briggs & Philip Clarke & Rury R. Holman & Alastair Gray, 2020. "Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data," Medical Decision Making, , vol. 40(4), pages 460-473, May.
    7. Joke Bilcke & Philippe Beutels & Marc Brisson & Mark Jit, 2011. "Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models," Medical Decision Making, , vol. 31(4), pages 675-692, July.
    8. Oluwatomi Adetunji & John Bischoff & Christopher J. Willy, 2018. "Managing system obsolescence via multicriteria decision making," Systems Engineering, John Wiley & Sons, vol. 21(4), pages 307-321, July.
    9. Jonah Popp & John A. Nyman & Xianghua Luo & Jill Bengtson & Katherine Lust & Lawrence An & Jasjit S. Ahluwalia & Janet L. Thomas, 2018. "Cost-effectiveness of enhancing a Quit-and-Win smoking cessation program for college students," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(9), pages 1319-1333, December.
    10. Heß, Michael (Ed.) & Schlieter, Hannes (Ed.), 2014. "Modellierung im Gesundheitswesen: Tagungsband des Workshops im Rahmen der Modellierung 2014," ICB Research Reports 57, University Duisburg-Essen, Institute for Computer Science and Business Information Systems (ICB).
    11. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    12. Bernhard Ultsch & Oliver Damm & Philippe Beutels & Joke Bilcke & Bernd Brüggenjürgen & Andreas Gerber-Grote & Wolfgang Greiner & Germaine Hanquet & Raymond Hutubessy & Mark Jit & Mirjam Knol & Rüdiger, 2016. "Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community," PharmacoEconomics, Springer, vol. 34(3), pages 227-244, March.
    13. Hossein Haji Ali Afzali & Laura Bojke & Jonathan Karnon, 2018. "Model Structuring for Economic Evaluations of New Health Technologies," PharmacoEconomics, Springer, vol. 36(11), pages 1309-1319, November.
    14. Salah Ghabri & Françoise F. Hamers & Jean Michel Josselin, 2016. "Exploring Uncertainty in Economic Evaluations of Drugs and Medical Devices: Lessons from the First Review of Manufacturers’ Submissions to the French National Authority for Health," PharmacoEconomics, Springer, vol. 34(6), pages 617-624, June.
    15. Becky Pennington & Alex Filby & Lesley Owen & Matthew Taylor, 2018. "Smoking Cessation: A Comparison of Two Model Structures," PharmacoEconomics, Springer, vol. 36(9), pages 1101-1112, September.
    16. Gemma E. Shields & Mark Wilberforce & Paul Clarkson & Tracey Farragher & Arpana Verma & Linda M. Davies, 2022. "Factors Limiting Subgroup Analysis in Cost-Effectiveness Analysis and a Call for Transparency," PharmacoEconomics, Springer, vol. 40(2), pages 149-156, February.
    17. Fernando Alarid-Escudero & Richard F. MacLehose & Yadira Peralta & Karen M. Kuntz & Eva A. Enns, 2018. "Nonidentifiability in Model Calibration and Implications for Medical Decision Making," Medical Decision Making, , vol. 38(7), pages 810-821, October.
    18. Koen Degeling & Maarten J. IJzerman & Mariel S. Lavieri & Mark Strong & Hendrik Koffijberg, 2020. "Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process," Medical Decision Making, , vol. 40(3), pages 348-363, April.
    19. Peter J. Dodd & Jeff J. Pennington & Liza Bronner Murrison & David W. Dowdy, 2018. "Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation," Medical Decision Making, , vol. 38(8), pages 930-941, November.
    20. Annika Hoyer & Sophie Kaufmann & Ralph Brinks, 2019. "Risk factors in the illness-death model: Simulation study and the partial differential equation about incidence and prevalence," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-10, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:32:y:2012:i:5:p:701-711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.