Preferences for Biologic Treatments: A Discrete Choice Experiment Survey of Canadians with Severe Asthma
Bibliographic record
Abstract
Purpose: The safety and efficacy of biologics for severe asthma have been demonstrated in clinical trials, and subsequent economic evaluations have established their value from a population perspective. Insight into patient preferences for attributes of biologic treatments can inform treatment-related decisions and promote adherence. However, such data are limited in Canada, and no willingness-to-pay (WTP) data exists. This study aimed to quantify the strength of preferences of those with severe asthma for attributes of biologic treatments. Patients and Methods: Canadians with severe asthma completed a discrete choice experiment (DCE) consisting of 15 choice tasks and six biologic treatment attributes (improving daily activities, controlling other health conditions, frequency of administration, monthly out-of-pocket costs, reducing attack frequency, and reducing rescue inhaler use). Odds ratios (OR) and 95% confidence intervals (CI), and WTP (the marginal rate of substitution of attributes for money) were estimated using a conditional logistic regression. Results: Ninety-seven eligible and unique participants completed the survey (70.1% female; mean [SD] age: 54.6 [14.4]; 48.4% ever used biologics). A dramatic (vs slight) improvement in daily activities increased the odds of a biologic being preferred by 78% (OR 1.78, 95% CI 1.48, 2.14), and a $100 increase in monthly out-of-pocket costs decreased the odds by 64% (OR 0.64, 95% CI 0.61, 0.67). On average, WTP was an extra $129 CAD in monthly out-of-pocket costs for a dramatic (vs slight) improvement in daily activities. WTP for a hypothetical biologic treatment was an extra $430 CAD in monthly out-of-pocket costs. Conclusion: Canadians with severe asthma prefer biologic treatments that dramatically improve daily activities and have lower out-of-pocket costs. This DCE is the first to include a cost attribute and estimate WTP. These data can help inform decision-making when considering access to new biologic treatments for severe asthma and clinicians when helping patients select treatments for severe asthma.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".