Regulatory decisions to approve, restrict development, or halt the marketing of new pharmaceuticals require evaluating the balance between benefits and risks, given the available evidence at a point in time. In response to concerns about how such decisions are reached, there is increasing interest in using patients' perceptions of the benefits of treatment features and their tolerance for possible risks to help inform regulatory decisions. Stated-choice methods, which measure stated preferences and are sometimes called discrete-choice experiments or conjoint analysis, are often the most valid and reliable techniques available for quantifying patient preferences because data on actual choices are limited. This introduction discusses how to adapt and apply stated-choice methods to quantitative benefit-risk analysis. We outline the conceptual framework for measuring patient preferences and the requirements for developing and administering a valid survey instrument. We also provide a numerical example illustrating how stated-choice data can be used to quantify benefit-risk tradeoff preferences. Finally, we discuss some limitations and practical considerations involving its use for regulatory and clinical decision making.
