Assessing Patient Decision-Making on Biologic and Small-Molecule Therapies in Inflammatory Bowel Diseases: Insights From a Conjoint Analysis in the United States, Canada, and the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Recent drug approvals have increased the number of therapies available for inflammatory bowel disease (IBD), making it difficult for patients to navigate available treatment options. We examined patient decision-making surrounding biologic and small-molecule therapies in an international cohort of patients from the United States, Canada, and the United Kingdom using conjoint analysis (CA), a form of tradeoff analysis examining how respondents make complex decisions. METHODS: We performed a CA survey that quantified the relative importance of therapy attributes (eg, efficacy, adverse effects) in decision-making. Patients with IBD were recruited from the general population and through specialty IBD clinics. We used a hierarchical Bayes analysis to model individual patients' preferences and compared the relative importance of medication attributes between countries and practice settings. Using a series of multivariable linear regression models, we assessed whether demographic and clinical characteristics (eg, IBD subtype, severity) predicted how patients made decisions. RESULTS: Overall, 1077 patients in 3 countries completed the survey. No differences in the relative importance of medication attributes were observed between the 3 countries' general IBD populations. However, efficacy was more important for patients in the US-based IBD specialty care cohort than for the general IBD population (29% and 23% importance, respectively; P < 0.0001). A few demographic and clinical characteristics were associated with small changes in individual preferences. CONCLUSIONS: In this large international CA study, patients prioritized efficacy as the most important therapeutic attribute. Decision-making seemed to be highly personalized in that therapeutic preferences were hard to predict based on patient 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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".