Resources available to fund research are limited. Whether in national funding organizations, such as the National Institute of Health, in pharmaceutical companies or in not-for profit private foundations, decision makers face the challenge of prioritizing research based on their budget. The newly popular area of comparative effectiveness research (CER) provides an important example of the need for research prioritization. CER conducts head-to-head comparisons to determine which drugs, devices, and procedures are most effective and carry the lowest risk. The 2009 American Recovery and Reinvestment Act dedicates $1.1 billion to this end, which may enhance the resources available for public funding agencies including the National Institute of Health and the Agency for Healthcare Research and Quality, to support CER. Although many factors enter the decision making criteria that guide prioritization of CER within these organizations, probably the most important is the effect of such research on patient welfare. One way to assess such value is using value of information analysis (VOI).1,2 VOI is a systematic tool to quantify the value of research. It estimates the probability that research will generate new information that will change decisions compared to decisions that would be made in the absence of such research (for example, with current information). It then multiplies this probability by a quantitative measure of value that would result due to the change of this decision. Research that cannot change decisions or cannot change decisions of any importance (value) is deemed to have a value of zero. Therefore, VOI analyses use an expected value criteria to quantify value of research. In this article, we explore the concepts of VOI and discuss various avenues via which such analyses could be useful tools for decision making on research prioritization.
