• Journal Article

Quantifying women's stated benefit–risk trade-off preferences for IBS treatment outcomes


Johnson, F., Hauber, A., Ozdemir, S., & Lynd, L. (2010). Quantifying women's stated benefit–risk trade-off preferences for IBS treatment outcomes. Value in Health, 13(4), 418-423. DOI: 10.1111/j.1524-4733.2010.00694.x


Background: The Food and Drug Administration, currently, is exploring quantitative benefit–risk methods to support regulatory decision-making. A scientifically valid method for assessing patients' benefit–risk trade-off preferences is needed to compare risks and benefits in a common metric.

Objectives: The study aims to quantify the maximum acceptable risk (MAR) of treatment-related adverse events (AEs) that women with diarrhea-predominant irritable bowel syndrome (IBS) are willing to accept in exchange for symptom relief.

Methods: Research design: A stated-choice survey was used to elicit trade-off preferences among constructed treatment profiles, each defined by symptom severity and treatment-related AEs. Symptom attributes included frequency of abdominal pain and discomfort, frequency of diarrhea, and frequency of urgency. AE attributes included frequency of mild-to-moderate constipation and the risk of four possible serious AEs. Subjects: A Web-enabled survey was administered to 589 female US residents at least 18 years of age with a self-reported diagnosis of diarrhea-predominant IBS.

Measures: Preference weights and MAR were estimated using mixed-logit methods.

Results: Subjects were willing to accept higher risks of serious AEs in return for treatments offering better symptom control. For an improvement from the lowest to the highest of four benefit levels, subjects were willing to tolerate a 2.65% increase in impacted-bowel risk, but only a 1.34% increase in perforated-bowel risk.

Conclusions: Variation in MARs across AE types is consistent with the relative seriousness of the AEs. Stated-preference methods offer a scientifically valid approach to quantifying benefit–risk trade-off preferences that can be used to inform regulatory decision-making.