Decision tree or analytic models in a pharmacoeconomic context are used to represent the various decision points along treatment and disease patterns and alternatives. The best, most cost-effective outcomes can then be determined using advanced statistical analyses. Models, however, are only as good as the underlying assumptions used to create them. Our expert modeling staff have the necessary skills to assimilate the available data and expert opinion on a given pharmaceutical-related research question and use that information to develop a credible decision analytic model. Below is an example of such a model. (Please visit the Markov health state models page for more information on decision models used to study health states that recur and change as time goes on and the discrete event simulations page for information on models used to investigate individual patient experiences versus cohorts.)
