Decisions about which health-care interventions represent adequate value to collectively funded health-care systems are as widespread as they are unavoidable. In the case of new pharmaceuticals, many countries now require formal cost-effectiveness analysis to inform this decision-making process. This requires evidence on parameters associated with health-related utilities, treatment effects, resource use, and costs, for which data from available regulatory trials are invariably absent or highly uncertain. This uncertainty results from a number of factors including the predominance of intermediate end points in the clinical evidence-base and the limited period of follow-up of patients in clinical studies. Despite these imperfections in the evidence base, decisions about whether new pharmaceuticals are sufficiently cost-effective for reimbursement cannot be side-stepped. Data limitations do, however, require the use of rigorous analytical methods to support decision making. Probabilistic decision models and value of information analysis offer a means of structuring decision problems, synthesizing all available data, characterizing the uncertainty in the decision, quantifying the cost of uncertainty, and establishing the expected value of perfect information. This analytical framework is important because it addresses two fundamental questions about new pharmaceuticals. First, is the product expected to be cost-effective on the basis of existing evidence? Second, is additional research concerning the product itself cost-effective? In addressing these questions, the analytical framework can establish when sufficient evidence exists to sustain a claim for a new pharmaceutical to be cost-effective.