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Simulating economic factors in adjuvant breast cancer treatment

Author

Listed:
  • T Eldabi

    (Brunel University)

  • R J Paul

    (Brunel University)

  • S J E Taylor

    (Brunel University)

Abstract

When conducting an experimental study in healthcare systems, two problems are faced, those of uncertainty and complexity. Uncertainty is related to identifying variables for data collection (particularly if there are time and cost constraints on the modelling exercise). Complexity is related to the existence of many interacting variables (including treatment paths for patients, patient illnesses, side effects of treatments, etc.), each of a stochastic nature. This paper reports the usefulness of discrete event simulation modelling in exploring these issues. It focuses on the use of this form of simulation in supporting decision making in a randomised clinical trial (RCT). The objective of using simulation modelling is to help health economists identify the key factors active in the RCT through the development of a model of the healthcare related processes being studied by the RCT. This approach provides an opportunity to allow users to understand the role of these factors in the RCT. This research is carried out in the context of the Adjuvant Breast Cancer RCT.

Suggested Citation

  • T Eldabi & R J Paul & S J E Taylor, 2000. "Simulating economic factors in adjuvant breast cancer treatment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 465-475, April.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:4:d:10.1057_palgrave.jors.2600881
    DOI: 10.1057/palgrave.jors.2600881
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    Citations

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    Cited by:

    1. T Eldabi & R J Paul & T Young, 2007. "Simulation modelling in healthcare: reviewing legacies and investigating futures," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 262-270, February.
    2. K Katsaliaki & N Mustafee, 2011. "Applications of simulation within the healthcare context," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(8), pages 1431-1451, August.
    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. Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.

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