A weighted criterion-based approach to value assessment of oncology drugs.
Bibliographic record
Abstract
6627 Background: The rising cost of anti-cancer therapy has motivated recent efforts to quantify the overall value of new cancer treatments. Multi-criteria decision analysis offers a novel approach to establish an explicit framework to evaluate new cancer treatments. Methods: We recruited a diverse multi-stakeholder group who identified and weighted key criteria to establish the Drug Assessment Framework (DAF). Strength of evidence (SOE) modifiers deducted points for lower quality evidence. Through one-on-one meetings with stakeholders, face and content validity of the DAF were established in an iterative process. Construct validity assessed the degree to which DAF scores were associated with the pan-Canadian oncology drug review (pCODR) funding decisions and European Society for Medical Oncology Magnitude of Clinical Benefit score (ESMO-MCBS, version 1.1). Sensitivity analyses were performed on the final results. Results: The final validated DAF includes ten criteria: overall survival, progression-free survival, response rate, quality-of-life, toxicity, unmet need, equity, feasibility, disease severity and caregiver well-being. The first five clinical benefit criteria represent 64% of the total weight. DAF scores range from 0 to 300, reflecting both the expected impact of the drug and the quality of the supporting evidence. When the DAF was retrospectively applied to the last 60 drugs (in blinded fashion) reviewed by pCODR (2015-2018), the mean total DAF score was 94 (range, 18-179). Drugs with positive pCODR funding recommendation had higher DAF scores than drugs not recommended for reimbursement (103 vs. 63, t-test p = 0.0007). Funded drugs had fewer SOE points deducted than those that were not funded (median 0 vs. 24 points deducted, Wilcoxon p = 0.03). The correlation coefficient for DAF and ESMO-MCBS was 0.37 (95% CI, 0.10 to 0.59). Sensitivity analyses that varied the subjective criteria either positively or negatively did not change the results. Conclusions: Using a structured and explicit approach, a criterion-based valuation framework was designed and validated. The DAF can provide a transparent and consistent method to value and prioritize cancer drugs, in order 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 | 0.176 | 0.371 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.031 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".