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A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions

Author

Listed:
  • E Ekaette

    (University of Calgary)

  • R C Lee

    (University of Calgary
    University of Calgary
    Calgary Health Technology Implementation Unit
    Institute of Health Economics)

  • K-L Kelly

    (Alberta Cancer Board)

  • P Dunscombe

    (University of Calgary
    Alberta Cancer Board)

Abstract

Radiation treatment (RT) for cancer is a critical medical procedure that occurs in a complex environment that is subject to uncertainties and errors. We employed a simulation (a variant of Monte Carlo) model that followed a cohort of hypothetical breast cancer patients to estimate the probability of incorrect staging and treatment decisions. As inputs, we used a combination of literature information and expert judgement. Input variables were defined as probability distributions within the model. Uncertainties were propagated via simulation. Sensitivity and value-of-information analyses were then conducted to quantify the effect of variable uncertainty on the model outputs. We found a small but non-trivial probability that patients would be incorrectly staged and thus be subjected to inappropriate treatment. Some routinely used tests for staging and metastasis detection have very limited informational value. This work has implications for the methods used in cancer staging and subsequent risk assessment of treatment errors.

Suggested Citation

  • E Ekaette & R C Lee & K-L Kelly & P Dunscombe, 2007. "A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 177-185, February.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:2:d:10.1057_palgrave.jors.2602269
    DOI: 10.1057/palgrave.jors.2602269
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    Cited by:

    1. Brailsford, Sally & Vissers, Jan, 2011. "OR in healthcare: A European perspective," European Journal of Operational Research, Elsevier, vol. 212(2), pages 223-234, July.
    2. Edidiong Ekaette & Robert C. Lee & David L. Cooke & Sandra Iftody & Peter Craighead, 2007. "Probabilistic Fault Tree Analysis of a Radiation Treatment System," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1395-1410, December.

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