Comparing the Approval and Coverage Decisions of New Oncology Drugs in the United States and Other Selected Countries
Bibliographic record
Abstract
BACKGROUND: Global pharmaceutical sales for anticancer drugs were $74.4 billion in 2014, ranking first for drugs by therapeutic class. Countries may differ substantially in the approval and coverage decisions for anticancer drugs. OBJECTIVE: To compare the approval and coverage decisions for new anticancer drugs between the United States and 4 other countries: the United Kingdom, France, Australia, and Canada. METHODS: We identified all new anticancer drug indications approved by the FDA between January 1, 2009, and December 31, 2013. For each country, we reviewed the organizations, processes, criteria, and special considerations used to make approval and coverage decisions for the drug indications approved. We further quantified and compared the variations across the 5 countries in the approval and coverage decisions as of June 30, 2014, for new anticancer drug indications. RESULTS: "Of 45 anticancer drug indications approved in the United States between January 1, 2009, and December 31, 2013, 67% (30) were approved by the European Medicines Agency, and 53% (24) were approved in Canada and Australia before December 31, 2013. The U.S. Medicare program covered all 45 drug indications, and as of June 30, 2014, the United Kingdom covered 87% (26) of those approved in Europe- 58% (26) of the drug indications covered by Medicare. France, Canada, and Australia covered 42% (19), 29% (13), and 24% (11) of the drug indications covered by Medicare, respectively". [corrected]. CONCLUSIONS: Approval and reimbursement decisions vary substantially by country. The United States had the fewest access restrictions, and Australia was the most restrictive of the 5 countries that were examined. DISCLOSURES: No outside funding supported this study, and the authors report no conflicts of interest. Study concept and design were contributed primarily by Zhang, along with Hernandez and Hueser. All authors participated in data collection, and data interpretation was performed by Zhang and Hernandez, along with Hueser. The manuscript was written and revised by Zhang and Hernandez, along with Hueser.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".