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Selecting a decision model for economic evaluation: a case study and review

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  • Jonathan Karnon
  • Jackie Brown

Abstract

The increased use of modelling techniques as a methodological tool in the economic evaluation of health care technologies has, in the main, been limited to two approaches – decision trees and Markov chain models. The former are suited to modelling simple scenarios that occur over a short time period, whilst Markov chain models allow longer time periods to be modelled, in continuous time, where the timing of an event is uncertain. In the context of economic evaluation, a less well developed technique is discrete event simulation, which may allow even greater flexibility. Taking the economic evaluation of adjuvant therapies for breast cancer as an illustrative example, the process of building a decision tree, a Markov chain model, and a discrete event simulation model are described. The potential benefits and problems of each approach are discussed. The suitability of the modelling techniques to economic evaluations of health care programmes in general is then discussed. This section aims to illustrate the areas in which the alternative modelling methods may be most appropriately employed. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Jonathan Karnon & Jackie Brown, 1998. "Selecting a decision model for economic evaluation: a case study and review," Health Care Management Science, Springer, vol. 1(2), pages 133-140, October.
    • Handle: RePEc:kap:hcarem:v:1:y:1998:i:2:p:133-140
    DOI: 10.1023/A:1019090401655
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    Cited by:

    1. 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.
    2. Pereira, Miguel Alves & Machete, Inês Freire & Ferreira, Diogo Cunha & Marques, Rui Cunha, 2020. "Using multi-criteria decision analysis to rank European health systems: The Beveridgian financing case," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).

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