A framework for quantifying uncertainty about costs, effectiveness measures, and mar ginal cost-effectiveness ratios in complex decision models is presented. This type of application requires special techniques because of the multiple sources of information and the model-based combination of data. The authors discuss two alternative ap proaches, one based on Bayesian inference and the other on resampling. While com putationally intensive, these are flexible in handling complex distributional assumptions and a variety of outcome measures of interest. These concepts are illustrated using a simplified model. Then the extension to a complex decision model using the stroke- prevention policy model is described. Key words: cost-effectiveness analysis; sto chastic models; decision analysis; simulation; stroke; cerebrovascular disease. (Med Decis Making 1997;17:390-401)
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