Real-world Use of and Spending on New Oral Targeted Cancer Drugs in the US, 2011-2018
Bibliographic record
Abstract
Importance: Launch prices of new cancer drugs in the US have substantially increased in recent years despite growing concerns about the quantity and quality of evidence supporting their approval by the US Food and Drug Administration (FDA). Objective: To assess the use of and spending on new oral targeted cancer drugs among US residents with employer-sponsored insurance between 2011 and 2018, stratified by the strength of available evidence of benefit. Design, Setting, and Participants: In this cross-sectional study, dispensing claims for oral targeted cancer drugs first approved by the FDA between January 1, 2011, and December 31, 2018, were analyzed. The number of patients with drugs dispensed and the total payment for all claims were aggregated by calendar year, and these outcomes were arrayed according to evidence underlying FDA approvals, including pivotal study design (availability of randomized clinical trials) and overall survival (OS) benefit, as documented in drug labels. This study was conducted from July 17, 2019, to July 23, 2021. Main Outcomes and Measures: Annual and cumulative numbers of patients who had dispensing events, and annual and cumulative sums of payment for eligible drugs. Results: Of 37 348 patients who had at least 1 of the 44 new oral targeted drugs dispensed between 2011 and 2018, 21 324 were men (57.1%); mean (SD) age was 64.1 (13.1) years. Most individuals (36 246 [97.0%]) received drugs for which evidence from randomized clinical trials existed; however, a growing share of patients received drugs without documented OS benefit during the study period: from 12.7% in 2011 to 58.8% in 2018. Cumulative spending on all sample drugs totaled $3.5 billion by the end of 2018, of which 96.8% was spent on drugs that were approved based on a pivotal randomized clinical trial. Cumulative spending on drugs without documented OS benefit ($1.8 billion [51.6%]) surpassed that on drugs with documented OS benefit ($1.7 billion [48.4%]) by the end of 2018. Conclusions and Relevance: The findings of this cross-sectional study suggest that drugs used for treatment of cancer without documented OS benefits are adopted in the health system and account for substantial spending.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".