A brief introduction to the use of stated-choice methods to measure preferences for treatment benefits and risks

By F Johnson, A Hauber, Christine Poulos

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.


Johnson, F., Hauber, A., & Poulos, C. (2009). A brief introduction to the use of stated-choice methods to measure preferences for treatment benefits and risks. (RTI Press Publication No. RR-0009-0909). Research Triangle Park, NC: RTI Press. https://doi.org/10.3768/rtipress.2009.rr.0009.0909

© 2019 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


F JohnsonF. Reed Johnson, PhD, is a Senior Fellow and principal economist in RTI Health Solutions.

A HauberBrett Hauber, PhD, is an expert in health and environmental economics with more than 15 years of academic, research, and government experience. His primary area of specialization is conducting conjoint analyses and discrete-choice experiments to quantify preferences for medical interventions and health outcomes. He also has extensive experience conducting benefit-risk analysis of patients and other health care decision makers. His most recent applied work has included discrete-choice experiments of patient and physician benefit-risk preferences for treatments for conditions in therapeutic areas such as neurology, infectious diseases, women's health, gastrointestinal diseases, diabetes, and oncology.

Christine PoulosChristine M. Poulos, PhD, is a senior economist in Health Preference Assessment at RTI Health Solutions.

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