Ranking the Criteria Used in the Appraisal of Drugs for Reimbursement: A Stated Preferences Elicitation With Health Technology Assessment Stakeholders Across Jurisdictional Contexts
Bibliographic record
Abstract
OBJECTIVES: Our goal was to estimate the relative importance assigned to health technology assessment (HTA) criteria by stakeholders involved in the HTA process. HTA is an increasingly common framework used in the appraisal of drugs for public reimbursement. It identifies clinical, economic, social, and organizational criteria to be considered. The criteria can vary across jurisdictions and are typically appraised by multidisciplinary expert committees. Guidance on the relative weighing of criteria is often absent. METHODS: We elicited stakeholders' preferences using a single-scenario discrete choice experiment and a best-worst scaling model with conviction scores to assess the weights assigned to selected criteria by HTA stakeholders. We recruited 111 HTA stakeholders across multiple jurisdictions, including members of expert committees, clinical and economic experts, patients, and public payer representatives. Each judged twelve hypothetical cancer drug profiles for suitability for public funding and identified which characteristics were best and worst. In addition to standard discrete choice experiment and best-worst scaling models, we estimated a hybrid model to obtain a ranking of criteria by importance they played in the appraisal. RESULTS: A strong clinical benefit proved the most important criterion, followed by cost considerations, presence of adverse events, and availability of other treatments. The importance of clinical benefit was moderated by unmet need, adverse events, and number of patients. CONCLUSION: Policymakers might want to consider providing an explicit weighing scheme, or moving to a 2-stage selection process with an assessment of the quality of clinical evidence as a gatekeeping step for a full HTA review.
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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 | 0.062 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".