A stated preference survey to explore patient preferences for novel preventive migraine treatments
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore patient preference for attributes of calcitonin gene-related peptide (CGRP) inhibitors for the preventive treatment of migraine and to describe differences in treatment preferences between patients. BACKGROUND: CGRP inhibitors are a novel class of migraine drugs specifically developed for the preventive treatment of migraine. Clinicians should understand patient preferences for CGRP inhibitors to inform and support prescribing choices. METHODS: Patients with migraine in the US and Germany were recruited to participate in an online discrete choice experiment (DCE) survey, which presented hypothetical treatment choices using five attributes: mode of administration, side effects, migraine frequency, migraine severity, and consistency of treatment effectiveness. Attribute selection was informed by a literature review and semi-structured patient interviews (n = 35), and evaluated using patient cognitive debriefing interviews (n = 5). RESULTS: Of 680 who consented to participate, 506 participants completed the survey and were included in the study (US = 257; Germany = 249). Overall, participants placed highest importance (preference weight, beta = 1.65, p < 0.001) on the treatment's ability to reduce the severity of migraine (mild vs. unchanged severity), followed by consistent treatment effectiveness (beta = 1.13, p < 0.001), and higher chance of reduced migraine frequency (beta = 1.00, p < 0.001). Participants preferred an oral tablet every other day (beta = 1.00, p < 0.001) over quarterly infusion, quarterly injections (p = 0.019), or monthly injection (p < 0.001). Preference for all treatment attributes were heterogeneous, and the subgroup analyses found that participants naïve to CGRP monoclonal antibody treatments had a stronger preference for oral therapy compared to those with such experience (p = 0.006). CONCLUSION: In this DCE assessing CGRP inhibitors attributes, the main driver of patient choice was treatment effectiveness, specifically reduced migraine severity, and consistent treatment effectiveness. Further, patients exhibited an overall preference for an oral tablet every other day over injectables. Patients' experience with previous treatments informs the value they place on treatment characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".