Economic evaluation of trifluridine/tipiracil (TT) versus nivolumab (N) in patients with advanced/metastatic gastric cancer (GC) or gastro-esophageal junction cancer (GEJC) in Canada.
Bibliographic record
Abstract
376 Background: Systemic treatment options in pre-treated patients with advanced/metastatic GC and GEJC have historically been limited especially in the third line or more. Recent potential advances in the therapeutic landscape of this patient population include TT (TAGS study) and immune checkpoint inhibitors (ICI) such as N (ATTRACTION-2 study). There is an anticipated budgetary impact on healthcare systems within the context of these potentially funded options. An economic evaluation can be instrumental in choosing a regimen if survival and quality of life are felt to be comparable. Methods: A cost minimization analysis was performed in Canadian dollars ($) comparing TT and N respectively in advanced de novo and relapsed GC/GEJC cases diagnosed in 2017 and subsequently treated in the third line in Canada. Direct costs including drug acquisition costs, supportive medications, transfusions, laboratory tests, physician visits, pharmacy and nursing time (health resource utilization-HRU) costs were calculated utilizing Ontario data. Direct costs for treatable adverse events G3/4 > 5% were incorporated. The analysis assumed complete drug delivery and the number of target patients was derived from constructed schema. Results: Compared to TT, the use of N was associated with a higher direct cost by a difference of $2.63 million (M) in the third line for GC and GEJC in Canada, principally reflecting a greater drug acquisition cost.The direct costs ranged (IQR) from 3.65M - 10.62M for TT to 3.3M – 15.69 M for N. N also had a greater HRU cost at 2.7 times that of TT and this was 64% of the direct costs for N excluding drug cost versus 42% of the direct non drug costs for TT. Supportive care (GCSF and transfusions) were 34% of the direct cost for TT excluding drug cost. A sensitivity analysis was performed. Conclusions: N generated a higher direct cost both for drug acquisition cost and other direct costs especially in HRU. Despite the increased cost of supportive care for adverse events related to TT, the direct non drug costs were less for this option. The use of biomarkers predictive of response may reduce the potential cost burden of the use of ICIs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".