Transparency in economic evaluations

[A proposed guideline for economic evaluation of health technologies]

Over the last few years, economic evaluation of health technologies has become a major tool used by European health policy decision-makers to create strategies for prioritizing the allocation of health resources and the approval of new technologies. Spain was a pioneer in proposing the standardization of methodology applicable to economic evaluation studies. However, because health policy decision-makers refused to support the initiative, the methodology was never put into practice. In the medium term, evidence of the economic value of new health technologies financed by the national health system will probably be increasingly required. At that time, stakeholders and decision-makers will have to agree upon a clear and concise set of rules on the technical and methodological issues that must be followed by economic evaluations of health technologies. Consequently, we have provided guidelines and recommendations for producing first-rate economic evaluations. The recommendations appear under seventeen headings or sections. In each case, the recommended requirements to be satisfied by an economic evaluation of health technologies are provided and each recommendation is followed by a commentary, providing a justification and comparing and contrasting the proposal with other available alternatives.

Pharmacoeconomic concepts in antiplatelet therapy: understanding cost-effectiveness analyses using clopidogrel as an example

The rising cost of drug therapy has been accompanied by demands from payers for pharmacoeconomic studies to assess the value for money of new treatments. There are 5 types of such analyses: cost analysis (evaluates only costs); cost minimization (compares costs of treatments with identical outcomes); cost-effectiveness (evaluates the cost of treatment in relation to clinical benefit to derive a cost per outcome); cost utility (evaluates cost of treatment in relation to survival adjusted for quality of life,) and cost benefit (evaluates all clinical outcomes, such as prolonged life, in monetary terms). The most commonly used are cost-effectiveness and cost utility. The aim of this article is to provide the nonexpert reader with a basic understanding of these analyses, using pharmacoeconomic evaluations of clopidogrel for acute coronary syndromes as examples. Greater clinician understanding of pharmacoeconomic principles (and pitfalls) will enhance input of the physicians into the decision-making process to maximize the benefit of limited health care resources.

A review of health care models for coronary heart disease interventions

This article reviews models for the treatment of coronary heart disease (CHD). Whereas most of the models described were developed to assess the cost effectiveness of different treatment strategies, other models have also been used to extrapolate clinical trials, for capacity and resource planning, or to predict the future population with heart disease. In this paper we investigate the use of modelling techniques in relation to different types of health intervention, and we discuss the assumptions and limitations of these approaches. Many of the models reviewed in this paper use decision tree models for acute or short term interventions, and Markov or state transition models for chronic or long term interventions. Discrete event simulation has, however, been used for more complex whole system models, and for modelling resource-constrained interventions and operational planning. Nearly all of the studies in our review used cohort-based models rather than population based models, and therefore few models could estimate the likely total costs and benefits for a population group. Most studies used de novo purpose built models consisting of only a small number of health states. Models of the whole disease system were less common. The model descriptions were often incomplete. We recommend that the reporting of model structure, assumptions and input parameters is more explicit, to reduce the risk of biased reporting and ensure greater confidence in the model results.