Bibliographic record
Abstract
This paper examines the issue of prices, relative to value, for cancer drugs. The analysis focuses on the effects on manufacturer pricing incentives of insurance coverage, specifically, the effectiveness of patient cost sharing, incentives created by reimbursement rules for physician-dispensed drugs, and payer ability and incentives to negotiate discounts. For pharmacy-dispensed cancer drugs, both Medicare Part D prescription drug plans (PDPs) and private payers' pharmacy benefit managers are increasingly placing these drugs on specialty tiers that offer no leverage for negotiating discounts and imply often unaffordable cost sharing for patients who lack catastrophic coverage. Simulation analysis of financial risks faced by PDPs confirms their incentives to place costly drugs on specialty tiers if more preferred formulary placement would increase use, possibly because of adverse selection risk. Faced with largely price-insensitive consumers and payers, manufacturers would rationally charge high prices. This situation is exacerbated for physician-dispensed cancer drugs, where Medicare's average selling price plus 6% reimbursement rule favors high-priced drugs. Because U.S. payers do not require evidence on prices relative to value, U.S. data are unavailable to test whether prices are higher, relative to value, for cancer drugs than for other drugs. Evidence from the Canadian Common Drug Review on cost-utility values suggests that cancer drugs are relatively high priced, although conclusions are tentative because of very small samples and non-U.S. data. Making such outcomes-adjusted prices available in the U.S. would be helpful to physicians, payers, and patients and indirectly constrain pricing to align with value.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".