Early cost–effectiveness modeling for better decisions in public research investment of personalized medicine technologies

Abstract

Millions of dollars are spent on the development of new personalized medicine technologies. While these research costs are often supported by public research funds, many diagnostic tests and biomarkers are not adopted by the healthcare system due to lack of evidence on their cost–effectiveness. We describe a stepwise approach to conducting cost–effectiveness analyses that are performed early in the technology's development process and can help mitigate the potential risks of investment. Decision analytic modeling can identify the key drivers of cost effectiveness and provide minimum criteria that the technology needs to meet for adoption by public and private healthcare systems. A value of information analysis can quantify the added value of conducting more research to provide further evidence for policy decisions. These steps will allow public research funders to make better decisions on their investments to maximize the health benefits and to minimize the number of suboptimal technologies.