, Volume 23, Issue 4, pp 323–332 | Cite as

Pharmacoeconomic analyses using discrete event simulation

  • J. Jaime CaroEmail author
Current Opinion


To date, decision trees and Markov models have been the most common methods used in pharmacoeconomic evaluations. Both of these techniques lack the flexibility required to appropriately represent clinical reality. In this paper an alternative, more natural, way to model clinical reality — discrete event simulation — is presented and its application is illustrated with a real world example.

A discrete event simulation represents the course of disease very naturally, with few restrictions. Neither mutually exclusive branches nor states are required, nor is a fixed cycle. All relevant aspects can be incorporated explicitly and efficiently. Flexibility in handling perspectives and carrying out sensitivity analyses, including structural variations, is incorporated and the entire model can be presented very transparently. The main limitations are imposed by lack of data to fit realistic models.

Discrete event simulation, though rarely employed in pharmacoeconomics today, should be strongly considered when carrying out economic evaluations, particularly those aimed at informing policy makers and at estimating the budget impact of a pharmaceutical intervention.


Decision Tree Mood Stabiliser Discrete Event Simulation Pharmacoeconomic Analysis Clinical Reality 
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.



This work was supported, in part, by a grant from Astra Zeneca AB. The author has no conflicts of interest directly relevant to the contents of this manuscript.


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© Adis Data Information BV 2005

Authors and Affiliations

  1. 1.Caro Research InstituteConcordUSA

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