Health Preference Research in Europe: A Review of Its Use in Marketing Authorization, Reimbursement, and Pricing Decisions—Report of the ISPOR Stated Preference Research Special Interest Group
Bibliographic record
Abstract
OBJECTIVE: This study examines European decision makers' consideration and use of quantitative preference data. METHODS: The study reviewed quantitative preference data usage in 31 European countries to support marketing authorization, reimbursement, or pricing decisions. Use was defined as: agency guidance on preference data use, sponsor submission of preference data, or decision-maker collection of preference data. The data could be collected from any stakeholder using any method that generated quantitative estimates of preferences. Data were collected through: (1) documentary evidence identified through a literature and regulatory websites review, and via key opinion leader outreach; and (2) a survey of staff working for agencies that support or make healthcare technology decisions. RESULTS: Preference data utilization was identified in 22 countries and at a European level. The most prevalent use (19 countries) was citizen preferences, collected using time-trade off or standard gamble methods to inform health state utility estimation. Preference data was also used to: (1) value other impact on patients, (2) incorporate non-health factors into reimbursement decisions, and (3) estimate opportunity cost. Pilot projects were identified (6 countries and at a European level), with a focus on multi-criteria decision analysis methods and choice-based methods to elicit patient preferences. CONCLUSION: While quantitative preference data support reimbursement and pricing decisions in most European countries, there was no utilization evidence in European-level marketing authorization decisions. While there are commonalities, a diversity of usage was identified between jurisdictions. Pilots suggest the potential for greater use of preference data, and for alignment between decision makers.
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 | 0.198 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".