In probabilistic economic analysis, the uncertainty concerning input parameters is quantified, and determines the level of uncertainty over the optimal decision. Researchers from a wide range of disciplines employ mathematical models to simulate complex processes. Common through many such disciplines is the conduct of importance analysis to determine those input parameters that contribute most to the uncertainty over the optimal decision based on the results of the analysis. In this study, we compare a range of potential importance measures to see how they compare with methods used in economic analysis. Techniques were classified as variance/correlation, information, probability, entropy, or elasticity-based measures. A selection of the most commonly used measures were applied to an economic model of treatment for patients with Parkinson's disease. Techniques were evaluated in terms of their ranking of variables, complexity, and interpretation.