Value Assessment in Oncology Drugs: Funding of Drugs for Metastatic Breast Cancer in Canada
Bibliographic record
Abstract
Background: Life expectancy for women with metastatic breast cancer has improved since the early 2000s, in part because of the introduction of novel therapies, including chemotherapy, hormonal therapy, and targeted agents. However, those treatments can come at a cost for the patient (short- and long-term toxicities from treatment) and at a financial cost for the health care system. Given the increase in the number of costly anticancer agents being introduced into the clinical setting, the American Society of Clinical Oncology (asco) and the European Society for Medical Oncology (esmo) have developed a system to quantify the value of new cancer treatments in terms of benefit, toxicities, and costs. Methods: In our value-assessment analysis, we included drugs that were funded in Canada between 2012 and 2017 for metastatic breast cancer. We reviewed the clinical benefit of those agents (survival, progression, quality of life), their costs, their value according to the asco and esmo value frameworks, and their assessments from the pan-Canadian Oncology Drug Review [pcodr (in Canada, except Quebec)] and the Institut national d'excellence en santé et en services sociaux [iness (in Quebec)]. Results: Drugs funded in Canada showed variation in their asco net health benefit scores and esmo magnitude of clinical benefit scores, but all had a cost-effectiveness ratio greater than $100,000 per quality-adjusted life-year. The strength and magnitude of the clinical benefit (for example, overall survival benefit vs. progression-free survival benefit) was not necessarily associated with a higher value score. Conclusions: Although great progress has been made in developing value frameworks, use of those frameworks has to be refined to help patients and health care providers make informed decisions about the benefit of novel cancer therapies and to help policymakers make decisions about the societal benefit of funding those therapies.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".