A phamacoeconomic analysis of personalized dosing versus fixed dosing of pembrolizumab in first-line PD-L1 positive non-small cell lung cancer.
Bibliographic record
Abstract
9013 Background: In October 2016 pembrolizumab became the new standard of care for first-line treatment of patients with metastatic non-small cell lung cancer (mNSCLC) whose tumors express programmed death ligand 1 in at least 50% of cells. The FDA recommended dose is 200mg every three weeks. Multiple studies have demonstrated equivalent efficacy with weight-based doses between 2mg/kg to 10 mg/kg. The objective of this study was to compare the economic impact of using personalized dosing (2mg/kg) versus fixed dosing (200mg) in the first line setting of mNSCLC. Methods: We performed a budget impact analysis from the US societal perspective to compare fixed dosing with personalized dosing. We calculated the target population and weight of patients that would be treated with pembrolizumab annually in the first-line setting. Using survival curves from the KEYNOTE 024 trial with Weibull extrapolation we estimated the mean number of cycles that patients would receive. Using the Medicare average sales price we calculated the difference in cost between personalized and fixed dosing. Results: Our base case model demonstrates that the total annual cost of pembrolizumab with fixed dosing is US$ 3,440,127,429, and with personalized dosing it is US$ 2,614,496,846. The use of personalized dosing would lead to a 24% annual saving of US$ 825,630,583 in the United States. Conclusions: Personalized dosing of pembrolizumab may have the potential to save approximately 0.825 billion dollars annually in the United States, likely without impacting outcomes. This option should be considered for the first-line management of PD-L1 positive advanced lung cancer.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".