Multinational Analysis of Estimated Health Care Costs Related to Extended-Interval Fixed Dosing of Checkpoint Inhibitors
Bibliographic record
Abstract
Importance: New dosing options for immune checkpoint inhibitors have recently been approved by the US Food and Drug Administration (FDA), including fixed dosing with extended intervals. Although the dose intensity appears the same, there is expected to be some waste with extended-interval dosing, as some drug remains in the bloodstream once a decision to stop treatment is made. The economic impact of extended-interval fixed dosing is unknown compared with standard-interval fixed dosing. Objective: To analyze the potential health care costs of using extended-interval fixed dosing instead of standard-interval fixed dosing. Design, Setting, and Participants: This economic evaluation used a pharmacoeconomic model to simulate 2 cohorts of patients with platinum-resistant metastatic urothelial cancer receiving pembrolizumab as second-line therapy at different dosing intervals using 2020 pricing data. Data were analyzed from 2020 to 2022. Exposures: The simulated patients received FDA-approved regimens of either 200 mg every 3 weeks or 400 mg every 6 weeks. Main Outcomes and Measures: The progression-free survival curve from the KEYNOTE-045 trial was used to estimate treatment duration. Drug, imaging, and administration costs were included in analyses. Sensitivity analyses were performed to assess how different imaging frequencies would affect the model results. The potential overall costs of using the 2 different dosing strategies were assessed. The base case was set in the US, while sensitivity analyses were set in several other countries. Results: In the base case analysis, dosing every 6 weeks instead of every 3 weeks resulted in an estimated 8.9% increase in pembrolizumab costs for the health care payer. Accounting for a decrease in infusion costs would result in an estimated net additional cost of $7483 per patient in the US (7.9% cost increase). In the US, this would amount to an increase of approximately $28 million annually for health care payers. Similar percentages in cost estimate increases were found for health care payers around the world, such as in Israel, where the net additional cost would be $5491 per patient. Conclusions and Relevance: This economic evaluation assessed and quantified the potential increased costs related to extended-interval fixed dosing of pembrolizumab. The model method could be applied to other diseases and other drugs for which there has been a movement toward extended-interval dosing. Results may differ in other diseases owing to differing disease courses and patient profiles.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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".