US Trends In Anticancer Medicine Pricing And The Impact Of Competition, 2014–20
Bibliographic record
Abstract
Prices of anticancer medicines (including chemically synthesized medicines and biologics manufactured from living organisms) are a subject of concern, as they contribute to spending by health plans and patients. We describe prices and evaluate the impact of competition among 185 anticancer medicines, using IQVIA data from the period October 2014-February 2020. We calculated the price of each medicine, defined as volume-weighted wholesale costs net of prompt-pay discounts and gross of rebates, without and with inflation adjustment, and summarized them by patent status, formulation, and therapeutic class. We evaluated the relationship between prices and competition, using regression analyses. We also conducted event studies of the impact of generic entry on the prices of three medicines from distinct clinically relevant therapeutic classes. Over the course of the study period, medicine prices increased among brand-name products and decreased among generics and biosimilars. After exclusivity loss, generic and biosimilar medicine prices decreased, whereas brand-name prices remained stable. Event study results present more divergent price trends after exclusivity loss. Consistent with previous research, our study showed that competition may improve the affordability of medicines for payers and patients. However, this effect is limited for anticancer medicines because only a minority are available as generics and biosimilars.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".