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Overcoming structural uncertainty in computer models
Date:4/1/2014

described in the literature, methods for quantifying structural uncertainty are not as well developed. This is especially true in the context of health economic decision making, which is the focus of this paper. Here, models are used to predict future costs and health consequences of options to make decisions for resource allocation.

"In health economics decision analysis, the use of "law-based" computer models is common. Such models are used to support national health resource allocation decisions, and the stakes are therefore high," says Strong. "While it is usual in this setting to consider the uncertainty in model inputs, uncertainty in model structure is almost never formally assessed."

There are several approaches to managing model structural uncertainty. A primary approach is 'model averaging' in which predictions of a number of plausible models are averaged with weights based on each model's likelihood or predictive ability. Another approach is 'model calibration', which assesses a model based on its external discrepancies, that is, output quantities and how they relate to real, observed values. In the context of healthcare decisions, however, neither of these approaches is feasible since typically more than one model is not available for averaging, and observations on model outputs are not available for calibration.

Hence, the authors use a novel approach based on discrepancies within the model or "internal discrepancies" (as opposed to external discrepancies which are the focus of model calibration). Internal discrepancies are analyzed by first decomposing the model into a series of subunits or subfunctions, the outputs of which are intermediate model parameters that are potentially observable in the real world. Next, each sub-function is judged for certainty based on whether its output would equal the true value of the parameter from real-world observations. If a potential structural error is anticipated, a discrepancy term
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Contact: Karthika Muthukumaraswamy
karthika@siam.org
267-350-6383
Society for Industrial and Applied Mathematics
Source:Eurekalert  

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