OP21 Patient Values Project (PVP): Patient Preferences For Cancer Treatments To Inform A Framework Incorporating Patient Values Into Health Technology Assessment
Bibliographic record
Abstract
Introduction The methodology for explicitly incorporating patient preferences by expert committees engaged in deliberative health technology assessment (HTA) processes for drug reimbursement recommendations is a relatively unexplored area despite the growing emphasis on patient-reported outcomes and patient engagement. The Patient Values Project (PVP) aims to improve patient input to expert review committees and promote a better understanding of the patient perspective using quantitative data to support the rationale in assessing new cancer drugs. Using colorectal cancer as a starting point, the PVP aims to develop a framework to objectively incorporate quantitative patient values and preferences into Canada’s cancer drug HTA decision-making process. We report on results from the first phase. Methods In the first phase, we developed a bilingual survey informed by qualitative focus groups, literature review and feedback from clinicians, patients and experts. The survey includes background questions, general and cancer specific quality-of-life tools, two discrete choice experiments (DCE) and a best worst scaling (BWS) experiment. After pre-testing and pilot testing, the survey was administered across Canada to metastatic and non-metastatic colorectal cancer patients and caregivers, in addition to adults from the general population. In the next phases, we will use vignettes to explore how patient preferences could be incorporated explicitly into decision-making, and what approach to use in HTA submissions. Results DCE1 survey results (˜n=1,000) reflect trade-offs between health-related quality-of-life and survival; DCE2 results reflect trade-offs between treatment regimens, side effects and survival/risk of recurrence; BWS results ranked and weighted the tolerability of 25 possible side effects of treatment. We observed differences in preferences amongst the general population, patients with metastatic cancer, non-metastatic cancer and caregivers. Conclusions Patients have unique perspectives and preferences about what is important and of value to them, which may impact patient adherence to treatment. In the next phases, we will explore how this evidence from patient preferences can be translated into values that could potentially be incorporated as an explicit element of the deliberative process for HTA decision-making.
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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 | 0.165 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".