Economic aspects of nivolumab in non-small cell lung cancer (NSCLC): Lessons from real life.
Bibliographic record
Abstract
e18322 Background: Novel immunotherapy agents' costs have a significant impact on healthcare system budgets. Aside from the cost per dose of the compound, the total treatment cost (TTC) is affected by the duration of treatment (DOT). DOT in real life may differ significantly from that observed in the randomized clinical trials because of the differences in baseline patient characteristics and treatment patterns. Methods: Advanced NSCLC patients (pts) (n=192) treated with nivolumab 3mg/kg q2w (expanded access program/standard of care) at five Israeli cancer centers between January 2015 and March 2016 were included in the analysis. DOT and TTC were assessed in 2 groups (group A: ECOG PS 0/1, n=92; group B: ECOG PS ≥2, n=100). In addition, response for a subgroup of 49 pts was evaluated by RECIST, v.1.1. In this subgroup, DOT and TTC of treatment (Tx) beyond progression (PD) were assessed as well. Nivolumab cost per dose was calculated for a 78 kg pt based on current market price in Israel: 3 mg/kg X 78 kg = 240 mg = 11,250 NIS (2,993 USD). Results: Pt baseline characteristics: median age 67y (range, 41-99); males 68%; smokers 77%; ECOG PS ≥2 52%; Non-squamous/Squamous/NA 78%/19%/3%. 27% of pts continued nivolumab at the time of last follow-up. DOT and TTC are presented in the table below. Conclusions: DOT and TTC are similar for ECOG PS 0/1 and ECOG PS ≥2 pts. Tx beyond PD increases the TTC by 32%. These facts should be taken into consideration when evaluating budget impact of novel immunotherapy agents' implementation into routine practice. [Table: see text]
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".