HAQ-DI scores can be used to estimate HUI-3 and EQ-5D utility values for patients with RA in data obtained from studies where utility values have not been collected.
ObjectiveTo assess patients’ preferences for a range of disease-modifying therapy (DMT) attributes in multiple sclerosis (MS).DesignA cross-sectional observational study.SettingThe data reported were from 17 MS units throughout Spain.ParticipantsAdult patients with relapsing-remitting MS.Main outcomeA conjoint analysis was applied to assess preferences. A total of 221 patients completed a survey with 10 hypothetical DMT profiles developed using an orthogonal design and rating preferences from 1 (most acceptable) to 10 (least acceptable). Medication attributes included preventing relapse, preventing disease progression, side effect risk, route and frequency of administration.ResultsPatients placed the greatest relative importance on the side effect risk domain (32.9%), followed by route of administration (26.1%), frequency of administration (22.7%), prevention of disease progression (10.0%) and prevention of relapse (8.3%). These results were independent of the Expanded Disability Status Scale score. The importance assigned to side effect risk was highest for patients with a recent diagnosis. Patients who had previously received more than one DMT gave a higher importance to relapse rate reduction than patients receiving their first DMT.ConclusionsPatient DMT preferences were mainly driven by risk minimisation, route of administration and treatment schedule. The risk–benefit spectrum of available DMT for MS is becoming increasingly complicated. Understanding which treatment characteristics are meaningful to patients may help to tailor information for them and facilitate shared decision-making in clinical practice.
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A mapping algorithm was developed for mapping of AcroQoL to EQ-5D, using patient level data from three previously published studies, and including validation in the confirmatory sub-sample. Mean (SD) utilities index in this study population was estimated as 0.71 (0.28). Additional research may be needed to test this mapping algorithm in other acromegaly populations.
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