The Relative Importance of Clinical, Economic, Patient Values and Feasibility Criteria in Cancer Drug Reimbursement in Canada: A Revealed Preferences Analysis of Recommendations of the Pan-Canadian Oncology Drug Review 2011–2017
Bibliographic record
Abstract
BACKGROUND: Most Canadian provinces and territories rely on the pan-Canadian Oncology Drug Review (pCODR) to provide recommendations regarding public reimbursement of cancer drugs. The pCODR review process considers four dimensions of value-clinical benefit, economic evaluation, patient-based values and adoption feasibility-but they do not define weights for individual decision criteria or an acceptable threshold for any of the criteria. Given this implicit review process, it is of interest to understand which factors appear to carry the most weight in pCODR recommendations using a revealed preferences approach. METHODS: Using publicly available decision summaries (n = 91) describing submissions and resulting recommendations 2011-2017, we extracted ten attributes that characterized each submission. Using logistic regression, we identified statistically significant attributes and estimated their relative impact in final recommendations. RESULTS: Clinical aspects appear to carry the greatest weight in the decision to reject or not reject, along with aspects of patient value (treatments with no alternatives were less likely to be rejected). Cost effectiveness does not appear to play a role in the initial decision to reject or not reject but is critical in full versus conditional approvals. There is evidence of a maximum acceptable threshold of around $Can140,000 per quality-adjusted life-year (QALY) gained. CONCLUSION: A set of factors driving pCODR recommendations is identifiable, supporting the consistency of the review process. However, the implicit nature of the review process and the difficulty of extracting and interpreting some of the attribute levels used in the analysis suggests that the process may still lack full transparency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".