The importance of evidence-based health policy is widely acknowledged among health care professionals, patients and politicians. Health care resources available for medical procedures, including pharmaceuticals, are limited all over the world. Economic evaluations help to alleviate the burden of scarce resources by improving the allocative efficiency of health care financing. Reimbursement of new medicines is subject to their cost-effectiveness and affordability in more and more countries. There are three major approaches to calculate the cost-effectiveness of new pharmaceuticals. Economic analyses alongside pivotal clinical trials are often inconclusive due to the suboptimal collection of economic data and protocol-driven costs. The major limitation of observational naturalistic economic evaluations is the selection bias and that they can be conducted only after registration and reimbursement. Economic modelling is routinely used to predict the cost-effectiveness of new pharmaceuticals for reimbursement purposes. Accuracy of cost-effectiveness estimates depends on the quality of input variables; validity of surrogate end points; and appropriateness of modelling assumptions, including model structure, time horizon and sophistication of the model to differentiate clinically and economically meaningful outcomes. These economic evaluation methods are not mutually exclusive; in practice, economic analyses often combine data collection alongside clinical trials or observational studies with modelling. The need for pharmacoeconomic evidence has fundamentally changed the strategic imperatives of research and development (R&D). Therefore, professionals in pharmaceutical R&D have to be familiar with the principles of pharmacoeconomics, including the selection of health policy-relevant comparators, analytical techniques, measurement of health gain by quality-adjusted life-years and strategic pricing of pharmaceuticals.