Decisions in health care must be made, despite uncertainty about benefits, risks, and costs. Value of information analysis is a theoretically sound method to estimate the expected value of future quantitative research pertaining to an uncertain decision. If the expected value of future research does not exceed the cost of research, additional research is not justified, and decisions should be based on current evidence, despite the uncertainty. To assess the importance of individual parameters relevant to a decision, different value of information methods have been suggested. The generally recommended method assumes that the expected value of perfect knowledge concerning a parameter is estimated as the reduction in expected opportunity loss. This method, however, results in biased expected values and incorrect importance ranking of parameters. The objective of this paper is to set out the correct methods to estimate the partial expected value of perfect information and to demonstrate why the generally recommended method is incorrect conceptually and mathematically. Copyright

Bayesian decision theory, Economic evaluation, Simulation, Uncertainty, Value of information,
Health Economics
Erasmus MC: University Medical Center Rotterdam

Groot Koerkamp, B, Hunink, M.G.M, Stijnen, Th, & Weinstein, M.C. (2006). Identifying key parameters in cost-effectiveness analysis using value of information: A comparison of methods. Health Economics, 15(4), 383–392. doi:10.1002/hec.1064