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Medication profiles were assigned preference ratings from 0 (not acceptable) to 10 (most favorable). Medication attributes included a range of benefits, adverse effects, administration routes, and market durations. Analytical models used linear mixed-effects regression. Results: Participants showed the highest preference for medication profiles that would improve their symptoms (�� = 0.81�C1.03, P Obeticholic Acid order (�� = 0.12, P Integrase inhibitor but not on relapse prevention, the proven drug outcome. Patients were willing to accept some level of serious risk for certain types and amounts of benefits, and they strongly preferred daily oral administration over all other options. With numerous disease-modifying therapy (DMT) choices for relapsing-remitting multiple sclerosis (RRMS) now on the market, health-care decisions require patients and physicians to weigh a more complicated spectrum of treatment risks and benefits.1,2 Although improved outcomes are associated with early adoption and continued adherence to DMTs, patients with RRMS continue to have high discontinuation and low adherence rates.3�C6 All approved DMTs decrease relapse rates, and some have been shown to slow disease progression. In addition, most DMTs are injectable, although the newest drugs are oral. Adverse effect risks range from mild (injection-site reactions) to severe (liver failure, serious cardiac events, leukemia, and progressive multifocal leukoencephalopathy).7�C10 The differences in medication attributes and patient risk perception make it difficult for patients to select a treatment, resulting in barriers to DMT initiation and adherence.11 Preferences CDK9 when weighing risks and benefits have been studied by various methods, including standard gamble, willingness to pay, and time trade-off utility scores, all measuring the maximum acceptable risk for a health state in exchange for a chance at either a better health state or death. These widely used techniques, which follow the expected utility framework, are criticized for not accurately representing real decision making because people do not follow linear probability weighting when making decisions.12 Conjoint analysis does not assume linear weighting and has recently been successfully applied to health-related decisions requiring trade-offs of risks and benefits.