Patient preferences for the benefits and risks of insomnia medication in a clinical trial and real-world setting
Bibliographic record
Abstract
Abstract Background The PAtient preferences stUdy in inSomnia (PAUSe) sub-study used a discrete choice experiment (DCE) to assess treatment preferences in patients with chronic insomnia who participated in randomized, placebo-controlled trials (NCT03545191 or NCT03575104). We conducted a replication study (PAUSe II), including real-world patients with self-reported insomnia who were recruited via online panels. Methods The DCE required patients to choose between insomnia treatments with different attributes. The aim of the study was to assess preferences for insomnia treatment in the real-world population versus trial setting (PAUSe population). In PAUSe, there was no option to indicate a preference for not receiving treatment. To assess if the addition of an option to ‘opt out’ of treatment had an impact on patient preferences, 20% of the real-world cohort were randomized to complete a version of the DCE that included an opt-out alternative. Relative attribute importance (RAI) and acceptable trade-offs between benefits and risks were obtained using a mixed logit model. Results In the real-world group ( n = 474), real-world opt-out group ( n = 131), and the trial group ( n = 602), the most important drivers of treatment preferences were improved daytime functioning and avoidance of withdrawal effects. The RAI for improved daytime functioning was 29.7%, 23.3%, and 33.6%, in the real-world group, real-world opt-out group, and trial group, respectively, and for avoidance of withdrawal effects was 33.5%, 23.3%, and 27.3%, respectively. Patients who had the opt-out alternative tended to select treatment over no treatment. Conclusions Despite some differences between groups, preferences in the treatment of insomnia were strongly driven by the desire to improve daytime functioning and avoid medication withdrawal effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".