Value assessment of oncology drugs using a weighted criterion‐based approach
Bibliographic record
Abstract
BACKGROUND: Globally, the rising cost of anticancer therapy has motivated efforts to quantify the overall value of new cancer treatments. Multicriteria decision analysis offers a novel approach to incorporate multiple criteria and perspectives into value assessment. METHODS: The authors recruited a diverse, multistakeholder group who identified and weighted key criteria to establish the drug assessment framework (DAF). Construct validity assessed the degree to which DAF scores were associated with past pan-Canadian Oncology Drug Review (pCODR) funding recommendations and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS; version 1.1) scores. RESULTS: The final DAF included 10 criteria: overall survival, progression-free survival, response rate, quality of life, toxicity, unmet need, equity, feasibility, disease severity, and caregiver well-being. The first 5 clinical benefit criteria represent approximately 64% of the total weight. DAF scores ranged from 0 to 300, reflecting both the expected impact of the drug and the quality of supporting evidence. When the DAF was applied to the last 60 drugs (with reviewers blinded) reviewed by pCODR (2015-2018), those drugs with positive pCODR funding recommendations were found to have higher DAF scores compared with drugs not recommended (103 vs 63; Student t test P = .0007). DAF clinical benefit criteria mildly correlated with ESMO-MCBS scores (correlation coefficient, 0.33; 95% CI, 0.009-0.59). Sensitivity analyses that varied the criteria scores did not change the results. CONCLUSIONS: Using a structured and explicit approach, a criterion-based valuation framework was designed to provide a transparent and consistent method with which to value and prioritize cancer drugs to facilitate the delivery of affordable cancer care.
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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 | 0.136 | 0.313 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.027 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".