National trends in post-launch cancer prescription drug prices and the impact of generic entry, 2014-2020.
Bibliographic record
Abstract
6598 Background: The high prices of branded ‘on patent’ cancer prescription drugs and the impact of competition on them is of importance to patients, physicians, payers and policymakers. The objective of this study is to quantify trends in the prices of all currently approved cancer drugs and to evaluate the impact of loss of exclusivity and generic entry on prices using national and contemporaneous data. Methods: Observational study of cancer drug prices from the IQVIA National Sales Perspectives from January 2014 to December 2020. The compound annual growth rate (CAGR) of drug prices was calculated for branded and generic drugs by therapeutic class (chemotherapy, targeted therapy, and supportive therapy). An event study empirical strategy and ordinary least squares regression was used to determine the relationship between the natural logarithm of prices and loss of exclusivity and generic entry. Results: The study cohort included 184 cancer drugs (37% chemotherapy, 51% targeted therapy, and 12% supportive therapy), of which 105 were always branded, 18 were always generic and 18 underwent loss of exclusivity and generic entry during the study period. Prices of branded chemotherapies and targeted therapies increased by 2.24% (0.79% CPI-adjusted) and 2.83% (1.07% CPI-adjusted) annually, whereas generics decreased by 12.63% (-14.14% CPI-adjusted) and 20.15% (-21.57% CPI-adjusted), respectively. Prices of branded supportive therapies decreased by 0.73% (-2.40% CPI-adjusted), while generics increased by 1.28% but decreased with CPI-adjustment (-0.45). Loss of exclusivity and generic entry was associated with statistically significant decreases in generic drug prices, but increases in branded drug prices after CPI-adjustment; these effects are concentrated in chemotherapies and targeted therapies. Conclusions: We found that cancer drug prices increased, after adjusting for inflation, particularly among branded chemotherapies and targeted therapies. Generic entry mitigates these price increases, but only a handful of cancer drugs experienced competition in the study period. Drug prices are key determinants of cancer-related spending, and many cancer patients remain underinsured. Although this data reflects net prices before discounts to providers and pharmacies, patient out-of-pocket costs are based on the list price. Our findings are critical to informing current efforts to improve cancer treatment affordability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".