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When to Use Discrete Event Simulation (DES) for the Economic Evaluation of Health Technologies? A Review and Critique of the Costs and Benefits of DES

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  • Jonathan Karnon
  • Hossein Haji Ali Afzali

Abstract

Modelling in economic evaluation is an unavoidable fact of life. Cohort-based state transition models are most common, though discrete event simulation (DES) is increasingly being used to implement more complex model structures. The benefits of DES relate to the greater flexibility around the implementation and population of complex models, which may provide more accurate or valid estimates of the incremental costs and benefits of alternative health technologies. The costs of DES relate to the time and expertise required to implement and review complex models, when perhaps a simpler model would suffice. The costs are not borne solely by the analyst, but also by reviewers. In particular, modelled economic evaluations are often submitted to support reimbursement decisions for new technologies, for which detailed model reviews are generally undertaken on behalf of the funding body. This paper reports the results from a review of published DES-based economic evaluations. Factors underlying the use of DES were defined, and the characteristics of applied models were considered, to inform options for assessing the potential benefits of DES in relation to each factor. Four broad factors underlying the use of DES were identified: baseline heterogeneity, continuous disease markers, time varying event rates, and the influence of prior events on subsequent event rates. If relevant, individual-level data are available, representation of the four factors is likely to improve model validity, and it is possible to assess the importance of their representation in individual cases. A thorough model performance evaluation is required to overcome the costs of DES from the users’ perspective, but few of the reviewed DES models reported such a process. More generally, further direct, empirical comparisons of complex models with simpler models would better inform the benefits of DES to implement more complex models, and the circumstances in which such benefits are most likely. Copyright Springer International Publishing Switzerland 2014

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  • Jonathan Karnon & Hossein Haji Ali Afzali, 2014. "When to Use Discrete Event Simulation (DES) for the Economic Evaluation of Health Technologies? A Review and Critique of the Costs and Benefits of DES," PharmacoEconomics, Springer, vol. 32(6), pages 547-558, June.
  • Handle: RePEc:spr:pharme:v:32:y:2014:i:6:p:547-558
    DOI: 10.1007/s40273-014-0147-9
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    References listed on IDEAS

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    1. Jorge Elgart & Joaquin Caporale & Lorena Gonzalez & Eleonora Aiello & Maximiliano Waschbusch & Juan Gagliardino, 2013. "Treatment of type 2 diabetes with saxagliptin: a pharmacoeconomic evaluation in Argentina," Health Economics Review, Springer, vol. 3(1), pages 1-9, December.
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    3. 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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    2. 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.
    3. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    4. 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.
    5. Penny R. Breeze & Hazel Squires & Kate Ennis & Petra Meier & Kate Hayes & Nik Lomax & Alan Shiell & Frank Kee & Frank de Vocht & Martin O’Flaherty & Nigel Gilbert & Robin Purshouse & Stewart Robinson , 2023. "Guidance on the use of complex systems models for economic evaluations of public health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1603-1625, July.
    6. Josephine Mauskopf, 2014. "Modelling Technique, Structural Assumptions, Input Parameter Values: Which Has the Most Impact on the Results of a Cost-Effectiveness Analysis?," PharmacoEconomics, Springer, vol. 32(6), pages 521-523, June.
    7. Keshtkaran, Mahsa & Churilov, Leonid & Hearne, John & Abbasi, Babak & Meretoja, Atte, 2016. "Validation of a decision support model for investigation and improvement in stroke thrombolysis," European Journal of Operational Research, Elsevier, vol. 253(1), pages 154-169.

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