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

E Ekaette (), R C Lee (), Kelly K-L and P Dunscombe
Additional contact information
E Ekaette: University of Calgary
R C Lee: University of Calgary
Kelly K-L: Alberta Cancer Board
P Dunscombe: University of Calgary

Journal of the Operational Research Society, 2007, vol. 58, issue 2, 177-185

Abstract: 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.

Keywords: decision analysis; medicine; probability; risk; Simulation (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1057/palgrave.jors.2602269

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