IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v18y2017i1d10.1007_s10198-015-0756-z.html
   My bibliography  Save this article

An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting

Author

Listed:
  • L. B. Standfield

    (Griffith University)

  • T. A. Comans

    (Griffith University)

  • P. A. Scuffham

    (Griffith University)

Abstract

Objectives To empirically compare Markov cohort modeling (MM) and discrete event simulation (DES) with and without dynamic queuing (DQ) for cost-effectiveness (CE) analysis of a novel method of health services delivery where capacity constraints predominate. Methods A common data-set comparing usual orthopedic care (UC) to an orthopedic physiotherapy screening clinic and multidisciplinary treatment service (OPSC) was used to develop a MM and a DES without (DES-no-DQ) and with DQ (DES-DQ). Model results were then compared in detail. Results The MM predicted an incremental CE ratio (ICER) of $495 per additional quality-adjusted life-year (QALY) for OPSC over UC. The DES-no-DQ showed OPSC dominating UC; the DES-DQ generated an ICER of $2342 per QALY. Conclusions The MM and DES-no-DQ ICER estimates differed due to the MM having implicit delays built into its structure as a result of having fixed cycle lengths, which are not a feature of DES. The non-DQ models assume that queues are at a steady state. Conversely, queues in the DES-DQ develop flexibly with supply and demand for resources, in this case, leading to different estimates of resource use and CE. The choice of MM or DES (with or without DQ) would not alter the reimbursement of OPSC as it was highly cost-effective compared to UC in all analyses. However, the modeling method may influence decisions where ICERs are closer to the CE acceptability threshold, or where capacity constraints and DQ are important features of the system. In these cases, DES-DQ would be the preferred modeling technique to avoid incorrect resource allocation decisions.

Suggested Citation

  • L. B. Standfield & T. A. Comans & P. A. Scuffham, 2017. "An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(1), pages 33-47, January.
    • Handle: RePEc:spr:eujhec:v:18:y:2017:i:1:d:10.1007_s10198-015-0756-z
    DOI: 10.1007/s10198-015-0756-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10198-015-0756-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10198-015-0756-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    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. Syed Salleh & Praveen Thokala & Alan Brennan & Ruby Hughes & Simon Dixon, 2017. "Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment," PharmacoEconomics, Springer, vol. 35(10), pages 989-1006, October.
    2. Lemoine, Coralie & Loubière, Sandrine & Boucekine, Mohamed & Girard, Vincent & Tinland, Aurélie & Auquier, Pascal, 2021. "Cost-effectiveness analysis of housing first intervention with an independent housing and team support for homeless people with severe mental illness: A Markov model informed by a randomized controlle," Social Science & Medicine, Elsevier, vol. 272(C).

    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. Matthew J. Glover & Edmund Jones & Katya L. Masconi & Michael J. Sweeting & Simon G. Thompson, 2018. "Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening," Medical Decision Making, , vol. 38(4), pages 439-451, 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. Syed Mohiuddin, 2014. "A Systematic and Critical Review of Model-Based Economic Evaluations of Pharmacotherapeutics in Patients with Bipolar Disorder," Applied Health Economics and Health Policy, Springer, vol. 12(4), pages 359-372, August.
    4. 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.
    5. 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.
    6. Olivier Ethgen & Baudouin Standaert, 2012. "Population–versus Cohort–Based Modelling Approaches," PharmacoEconomics, Springer, vol. 30(3), pages 171-181, March.
    7. John Graves & Shawn Garbett & Zilu Zhou & Jonathan S. Schildcrout & Josh Peterson, 2021. "Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation," Medical Decision Making, , vol. 41(4), pages 453-464, May.
    8. Ruth A. Lewis & Dyfrig Hughes & Alex J. Sutton & Clare Wilkinson, 2021. "Quantitative Evidence Synthesis Methods for the Assessment of the Effectiveness of Treatment Sequences for Clinical and Economic Decision Making: A Review and Taxonomy of Simplifying Assumptions," PharmacoEconomics, Springer, vol. 39(1), pages 25-61, January.
    9. 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.
    10. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    11. 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.
    12. Hossein Haji Ali Afzali & Jonathan Karnon & Jodi Gray, 2012. "A proposed model for economic evaluations of major depressive disorder," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(4), pages 501-510, August.
    13. 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.
    14. 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.
    15. Leslie Anne Campbell & John T. Blake & George Kephart & Eva Grunfeld & Donald MacIntosh, 2017. "Understanding the Effects of Competition for Constrained Colonoscopy Services with the Introduction of Population-level Colorectal Cancer Screening," Medical Decision Making, , vol. 37(2), pages 253-263, February.
    16. Stuart J. Wright & William G. Newman & Katherine Payne, 2019. "Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review," PharmacoEconomics, Springer, vol. 37(8), pages 1011-1027, August.
    17. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    18. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    19. Jen Kruger & Daniel Pollard & Hasan Basarir & Praveen Thokala & Debbie Cooke & Marie Clark & Rod Bond & Simon Heller & Alan Brennan, 2015. "Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models," Medical Decision Making, , vol. 35(7), pages 872-887, October.
    20. Sarah Bates & Thomas Bayley & Paul Norman & Penny Breeze & Alan Brennan, 2020. "A Systematic Review of Methods to Predict Weight Trajectories in Health Economic Models of Behavioral Weight-Management Programs: The Potential Role of Psychosocial Factors," Medical Decision Making, , vol. 40(1), pages 90-105, January.

    More about this item

    Keywords

    Discrete event simulation; Markov cohort; Cost-effectiveness; Dynamic queuing; DES;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General

    Statistics

    Access and download statistics

    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:spr:eujhec:v:18:y:2017:i:1:d:10.1007_s10198-015-0756-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.