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.)
