Probabilistic uncertainty analysis is a common means of evaluating pharmacoeconomic models and exploring decision uncertainty. Uncertain parameters are assigned probability distributions and analyses performed by Monte Carlo simulation. Correlations between input parameters are rarely accounted for despite recommendations from several guidelines. By outlining theoretical reasons for including correlations and showing numerous examples of existing correlations, we appeal to the analyst to consider input dependencies. Our objective is to review the available methods to do so, give technical details on implementation and show, by using examples of published studies, the effect input correlations have on model outputs.
A hierarchy of methods for dealing with correlations in Monte Carlo simulation is presented and used. The choice of method depends on the amount of information available on dependency and consists of functional modeling, joint distributions/copulas, and coupling of marginal distributions.
We induced input correlation with various methods and showed that in most cases the choice of optimal decision remained the same as in the independent scenario. There was, however, a significant change in the value of further information because of inducing input correlations. The results were similar for various dependency structures and were mainly a function of the strength of correlation, as measured by the linear correlation coefficient.
Probabilistic uncertainty analysis reflects joint uncertainty across input parameters only when dependence among input parameters is accounted for.