Design, Conduct and Use of Patient Preference Studies in the Medical Product Life Cycle
Bibliographic record
Abstract
Objectives: To investigate stakeholder perspectives on how patient preference studies \n(PPS) should be designed and conducted to allow for inclusion of patient preferences in \ndecision-making along the medical product life cycle (MPLC), and how patient preferences \ncan be used in such decision-making. \nMethods: Two literature reviews and semi-structured interviews (n = 143) with healthcare \nstakeholders in Europe and the US were conducted; results of these informed the design \nof focus group guides. Eight focus groups were conducted with European patients, \nindustry representatives and regulators, and with US regulators and European/Canadian \nhealth technology assessment (HTA) representatives. Focus groups were analyzed \nthematically using NVivo. \nResults: Stakeholder perspectives on how PPS should be designed and conducted \nwere as follows: 1) study design should be informed by the research questions and patient \npopulation; 2) preferred treatment attributes and levels, as well as trade-offs among \nattributes and levels should be investigated; 3) the patient sample and method should \nmatch the MPLC phase; 4) different stakeholders should collaborate; and 5) results from \nPPS should be shared with relevant stakeholders. The value of patient preferences in \ndecision-making was found to increase with the level of patient preference sensitivity of \ndecisions on medical products. Stakeholders mentioned that patient preferences are hardly \nused in current decision-making. Potential applications for patient preferences across \nindustry, regulatory and HTA processes were identified. Four applications seemed most \npromising for systematic integration of patient preferences: 1) benefit-risk assessment \nby industry and regulators at the marketing-authorization phase; 2) assessment of major contribution to patient care by European regulators; 3) cost-effectiveness analysis; and 4) \nmulti criteria decision analysis in HTA. \nConclusions: The value of patient preferences for dec
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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 | 0.202 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".