Exploring perceptions of using preference elicitation methods to inform clinical trial design in rheumatology: A qualitative study and OMERACT collaboration
Bibliographic record
Abstract
BACKGROUND: Clinical trial design requires value judgements and understanding patient preferences may help inform these judgements, for example when prioritizing treatment candidates, designing complex interventions, selecting appropriate outcomes, determining clinically important thresholds, or weighting composite outcomes. Preference elicitation methods are quantitative approaches that can estimate patients' preferences to quantify the absolute or relative importance of outcomes or other attributes relevant to the decision context. We aimed to explore stakeholder perceptions of using preference elicitation methods to inform judgements when designing clinical trials in rheumatology. METHODS: We conducted 1-on-1 semi-structured interviews with patients with rheumatic diseases and rheumatology clinicians/researchers, recruited using purposive and snowball sampling. Participants were provided pre-interview materials, including a video and a document, to introduce the topic of preference elicitation methods and case examples of potential applications to clinical trials. Interviews were conducted via Zoom and were audio-recorded and transcribed. We used thematic analysis to analyze our data. RESULTS: We interviewed 17 patients and 9 clinicians/researchers, until data and inductive thematic saturation were achieved within each group. Themes were grouped into overall perceptions, barriers, and facilitators. Patients and clinicians/researchers generally agreed that preference elicitation studies can improve clinical trial design, but that many considerations are required around preference heterogeneity and feasibility. A key barrier identified was the additional resources and expertise required to measure and incorporate preferences effectively in trial design. Key facilitators included developing guidance on how to use preference elicitation to inform trial design, as well as the role of external decision-makers in developing such guidance, and the need to leverage the movement towards patient engagement in research to encourage including patient preferences when designing trials. CONCLUSION: Our findings allowed us to consider the potential applications of patient preferences in trial design according to stakeholders within rheumatology who are involved in the trial process. Future research should be conducted to develop comprehensive guidance on how to meaningfully include patient preferences when designing clinical trials in rheumatology. Doing so may have important downstream effects for shared decision-making, especially given the chronic nature of rheumatic diseases.
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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 | 0.188 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".