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Each year new pharmaceutical products are introduced as a consequence of technological advances. For example, in the United States, between 1990 and 2004, the U.S. Food and Drug Administration approved 431 new drugs with new molecular entities (NMEs). On average, nearly 29 NMEs are introduced in the U.S. pharmaceutical market annually. These new drugs extend the capability of medicine to treat diseases. New drugs developed in the United States and other high-income countries are often soon introduced in other countries, particularly if the new drug is a life-saving innovation or represents a substantial improvement over existing drugs. Improving the public's health of population is a universal goal around the world, albeit limited by many countries' ability to afford the most recently developed pharmaceuticals.
Adopting new and more effective drugs is costly. In recent years the growth rate of spending on pharmaceuticals in many countries has far exceeded the growth rate of overall health spending. Taking the median of OECD (Organization for Economic Cooperation and Development) countries as an example, during 1990–2000 the average annual growth rate of spending per capita on pharmaceuticals was 4.5%, while the average annual growth rate of health care expenditure per capita was only 3.1% (Anderson et al. 2003). As a result, increased spending on pharmaceuticals has become a major driving force of rising personal health care expenditures in many countries.
The objective of this paper is to illustrate the advantages of the Bayesian approach in quantifying, presenting, and reporting scientific evidence and in assisting decision making. Three basic components in the Bayesian framework are the prior distribution, likelihood function, and posterior distribution. The prior distribution describes analysts' belief a priori; the likelihood function captures how data modify the prior knowledge; and the posterior distribution synthesizes both prior and likelihood information. The Bayesian approach treats the parameters of interest as random variables, uses the entire posterior distribution to quantify the evidence, and reports evidence in a “probabilistic” manner. Two clinical examples are used to demonstrate the value of the Bayesian approach to decision makers. Using either an uninformative or a skeptical prior distribution, these examples show that the Bayesian methods allow calculations of probabilities that are usually of more interest to decision makers, e.g., the probability that treatment A is similar to treatment B, the probability that treatment A is at least 5% better than treatment B, and the probability that treatment A is not within the “similarity region” of treatment B, etc. In addition, the Bayesian approach can deal with multiple endpoints more easily than the classic approach. For example, if decision makers wish to examine mortality and cost jointly, the Bayesian method can report the probability that a treatment achieves at least 2% mortality reduction and less than $20,000 increase in costs. In conclusion, probabilities computed from the Bayesian approach provide more relevant information to decision makers and are easier to interpret.
Until the mid-1980s, most economic analyses of healthcare technologies were based on decision theory and used decision-analytic models. The goal was to synthesize all relevant clinical and economic evidence for the purpose of assisting decision makers to efficiently allocate society's scarce resources. This was true of virtually all the early cost-effectiveness evaluations sponsored and/or published by the U.S. Congressional Office of Technology Assessment (OTA) (15), Centers of Disease Control and Prevention (CDC), the National Cancer Institute, other elements of the U.S. Public Health Service, and of healthcare technology assessors in Europe and elsewhere around the world. Methodologists routinely espoused, or at minimum assumed, that these economic analyses were based on decision theory (8;24;25). Since decision theory is rooted in—in fact, an informal application of—Bayesian statistical theory, these analysts were conducting studies to assist healthcare decision making by appealing to a Bayesian rather than a classical, or frequentist, inference approach. But their efforts were not so labeled. Oddly, the statistical training of these decision analysts was invariably classical, not Bayesian. Many were not—and still are not—conversant with Bayesian statistical approaches.
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