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PharmacoEconomics

, Volume 32, Issue 6, pp 547–558 | Cite as

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

  • Jonathan KarnonEmail author
  • Hossein Haji Ali Afzali
Review Article

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.

Keywords

Discrete Event Simulation Disease Marker Discrete Event Simulation Model Model Performance Evaluation Complex Model Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors have no conflicts of interest that are directly relevant to the content of this article. The opinions expressed in the article are those of the authors. No direct sources of funding were used to prepare this article. The authors gratefully acknowledge Mr Nino Marciano and Ms Jodi Gray for facilitating the review of the literature.

Author contributions

JK designed the review, performed the review, analysed and interpreted the data. HH contributed to the design of the study, review of studies, analysis and interpretation of the data. JK drafted the manuscript. HH made suggestions for revision. All authors read and approved the final manuscript. JK acts as the guarantor for the overall content of this article.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Population HealthUniversity of AdelaideAdelaideAustralia

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