Accounting for Methodological, Structural, and Parameter Uncertainty in Decision- Analytic Models: A Practical Guide

Accounting for uncertainty is now a standard part of decision-analytic modeling and is recommended by many health technology agencies and published guide-lines. However, the scope of such analyses is often lim-ited, even though techniques have been developed for presenting the effects of methodological, structural, and parameter uncertainty on model results. To help bring these techniques into mainstream use, the authors pres-ent a step-by-step guide that offers an integrated approach to account for different kinds of uncertainty in the same model, along with a checklist for assessing the way in which uncertainty has been incorporated. The guide also addresses special situations such as when a source of uncertainty is difficult to parameterize, resources are limited for an ideal exploration of uncer