There has been rapidly growing interest in performing economic evaluations as part of Phase III clinical trials. Even prospective collection of economic data, however, cannot answer all of the questions health policy makers, providers, and the public have about the costs and effects of new drugs. For example, there may be a need to project economic data from clinical trials to populations, time periods, or settings that were not observed in the trial.
In these cases, decision analytic models may allow the projection of the costs and effects of drugs. These models can take the form of simple decision trees or they can be very complex models where the underlying disease processes as well as the interventions are modeled. They can be developed using data from observational studies or they can be developed with data from intervention studies.
These different approaches to decision analytic modeling are outlined, using as examples models for coronary heart disease, zidovudine for asymptomatic human immunodeficiency virus infection, and HA-1A monoclonal antibody for gram negative sepsis.
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