OBJECTIVES: Risk-tolerance measures from patient-preference studies typically focus on individual adverse events. We recently introduced an approach that extends maximum-acceptable risk (MAR) calculations to simultaneous maximum-acceptable risk thresholds (SMART) for multiple treatment-related risks. We apply these methods to three published discrete-choice experiments to evaluate the potential utility of SMARTs to inform regulatory decision making.

METHODS: We generate MAR estimates and SMART curves and compare them to trial-based benefit-risk profiles of select treatments for depression, psoriasis and thyroid cancer. We also extend SMART curves to include confidence intervals conveying levels of uncertainty.

RESULTS: In the treatment-resistant depression study, SMART curves with 70%-95% confidence intervals portray which combinations of two adverse-events would be considered acceptable. In the psoriasis example, the asymmetric confidence intervals for the SMART curve indicate that relying on independent MARs versus SMART curves when there are non-linear preferences can lead to decisions that accept greater risk of adverse events than patients would accept. The thyroid cancer application shows an example where the clinical incidence of each of three adverse events is lower than the single-event MARs for the expected treatment benefit, yet the collective adverse-event risk profile surpasses acceptable levels when considered jointly.

CONCLUSIONS: When evaluating conventional MARs where the observed incidences are near the estimated MARs or where preferences demonstrate diminishing marginal disutility of risk, conventional MAR estimates considered individually will overstate risk acceptance, which could lead to misinformed decisions potentially placing patients at greater risk of adverse events than they would accept.