Targeted Therapies in Non–Small-Cell Lung Cancer Management: No Cost-Effective Strategies?
Bibliographic record
Abstract
TO THE EDITOR: Using a Markov model and from the Canadian perspective, Dajlalov et al 1 reported the cost-effectiveness in advanced nonsquamous non–small-cell lung cancer (NSCLC) of EML4-ALK fusion testing in combination with targeted first-line crizotinib as well as the cost-effectiveness of first-line crizotinib compared with standard care in patients with known EML4-ALK positivity. The incremental costeffectiveness ratio (ICER) of these two strategies was Canadian $255,870 and $250,632, respecfively, per quality-adjusted life-year (QALY) gained, and the authors concluded that these strategies are not cost effective. Although the authors appropriately discussed the limitations of the study we have two general comments on this exciting topic. Modeling in economic analysis is a useful and a relevant way to obtain results in case when no head-to-head trials have been performed, but specific heed needs to be paid to the key assumptions supporting the model. In the analysis by Dajlalov et al, 1 the standardcare strategy was cisplatin-gemcitabine doublet as first-line therapy, with pemetrexed and erlotinib as second- and third-line therapies. But the recommendations for first-line treatment of nonsquamous NSCLC also included pemetrexed concurrently with cisplatin and as an alternative, according to patient choice, bevacizumab combined with a paclitaxel-carboplatin regimen. Maintenance with bevacizumab or pemetrexed and switch maintenance with pemetrexed until progression are also acceptable options. Taking these recommendations into account impacts the standard of care, especially in costs, which increase from Canadian $1,527 to nearly $8,000 per chemotherapy cycle when using pemetrexed or bevacizumab in the first-line as well the maintenance setting. Unfortunately, in the sensitivity analysis, the cost of standard-care chemotherapy was not tested. Even if only a small proportion of patients received these high-cost drugs, it will probably significantly impact the ICER of both tested strategies. We also have a general comment about the interpretation of the ICER and the discrepancy between our clinical experience with crizotinib in patients with EML4-ALK positive NCSLC and the disappointing results of the economic analysis. In fact, these results should be compared with the ICER of recent strategies for management in NSCLC. For nonsquamous NSCLC in the United States in first-line setting, 2 the ICER of treatment with a cisplatin-pemetrexed doublet compared with carboplatin-paclitaxel and the ICER of treatment with carboplatin-paclitaxel-bevacizumab compared with cisplatinpemetrexed were $178,613 and $300,000, respectively, per life-year gained (LYG)—approximately $340,000 and $566,000, respectively, per QALY. Using pemetrexed as maintenance therapy 3 compared with observation led to an ICER of $122,371 per LYG (approximately $230,000 per QALY). In second-line setting in Canada, the ICER for erlotinib treatment in the
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".