The Application of Preference Elicitation Methods in Clinical Trial Design to Quantify Trade-Offs: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Patients can express preferences for different treatment options in a healthcare context, and these can be measured with quantitative preference elicitation methods. OBJECTIVE: Our objective was to conduct a scoping review to determine how preference elicitation methods have been used in the design of clinical trials. METHODS: We conducted a scoping review to identify primary research studies, involving any health condition, that used quantitative preference elicitation methods, including direct utility-based approaches, and stated preference studies, to value health trade-offs in the context of clinical trial design. Studies were identified by screening existing systematic and scoping reviews and with a primary literature search in MEDLINE from 2010 to the present. We extracted study characteristics and the application of preference elicitation methods to clinical trial design according to the SPIRIT checklist from primary studies and summarized the findings descriptively. RESULTS: We identified 18 eligible studies. The included studies applied patient preferences to five areas of clinical trial design: intervention selection (n = 1), designing N-of-1 trials (n = 1), outcome selection and weighting composite and ordinal outcomes (n = 12), sample size calculations (n = 2), and recruitment (n = 2). Using preference elicitation methods led to different decisions being made, such as using preference-weighted composite outcomes instead of equally weighted composite outcomes. CONCLUSION: Preference elicitation methods are infrequently used to design clinical trials but may lead to changes throughout the trial that could affect the evidence generated. Future work should consider measurement challenges and explore stakeholder perceptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.187 | 0.355 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".