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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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).

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    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

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