A genuine need or nice to have? Understanding HTA representatives’ perspectives on the use of patient preference data
Bibliographic record
Abstract
OBJECTIVES: The roles and potential value of patient preference (PP) data in health technology assessment (HTA) remain to be fully realized despite an expanding literature and various efforts to establish their utility. This article reports lessons learned through a series of collaborative workshops with HTA representatives, organized by the Health Technology Assessment International's Patient Preferences Project Subcommittee. METHODS: Five online workshops were conducted between June 2022 and June 2023, seeking to facilitate collaborative learning and reflection on ways that PP data can be integrated into HTA. Participants included nine HTA representatives from the United States, Canada, Australia, England, and the Netherlands. Workshops were recorded, transcribed, and thematically analyzed. RESULTS: Despite appreciating the value of PP data, participants were ambivalent about their use in HTA. Some felt that they were already getting the information they needed from the cost-effectiveness analysis or existing patient involvement processes. Others thought that PP data would be very helpful at the initial and final stage of the decision-making process and, particularly, in the following cases: (a) when technology has important non-health benefits; (b) when the clinical and/or cost-effectiveness evidence is marginal; and (c) when treatment is indicated for a large and heterogeneous population. Issues related to the validity and reliability of PP studies were frequently raised, with preference heterogeneity at the core of these concerns. CONCLUSIONS: Collaborating with HTA representatives in the "co-creation" of PP research can help address their concerns and facilitate mutual learning about how PP data can be used in HTA.
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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 | 0.197 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.019 |
| 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".