Principles of pharmacoeconomic analysis of drug therapy

Economic analyses have become increasingly important in healthcare in general and with respect to pharmaceuticals in particular. If economic analyses are to play an important and useful role in the allocation of scarce healthcare resources, then such analyses must be performed properly and with care. This article outlines some of the basic principles of pharmacoeconomic analysis. Every analysis should have an explicitly stated perspective, which, unless otherwise justified, should be a societal perspective. Cost minimisation, cost-effectiveness, cost-utility and cost-benefit analyses are a family of techniques used in economic analyses. Cost minimisation analysis is appropriate when alternative therapies have identical outcomes, but differ in costs. Cost-effectiveness analysis is appropriate when alternative therapies differ in clinical effectiveness but can be examined from the same dimension of health outcome. Cost-utility analysis can be used when alternative therapies may be examined using multiple dimensions of health outcome, such as morbidity and mortality. Cost-benefit analysis requires the benefits of therapy to be described in monetary units and is not usually the technique of choice. The technique used in an analysis should be described and explicitly defended according to the problem being examined. For each technique, the method of determining costs is the same; direct, indirect, and intangible costs can be considered. The specific costs to be used depend on the analytical perspective; a societal perspective implies the use of both direct and indirect economic costs. A modelling framework such as a decision tree, influence diagram, Markov chain, or network simulation must be used to structure the analysis explicitly. Regardless of the choice of framework, all modelling assumptions should be described. The mechanism of data collection for model inputs must be detailed and defended. Models must undergo careful verification and validation procedures. Following baseline analysis of the model, further analyses should examine the role of uncertainty in model assumptions and data.