Cost Effectiveness of <i>EML4-ALK</i> Fusion Testing and First-Line Crizotinib Treatment for Patients With Advanced <i>ALK</i>-Positive Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: ALK-targeted therapy with crizotinib offers significant improvement in clinical outcomes for the treatment of EML4-ALK fusion-positive non-small-cell lung cancer (NSCLC). We estimated the cost effectiveness of EML4-ALK fusion testing in combination with targeted first-line crizotinib treatment in Ontario. PATIENTS AND METHODS: A cost-effectiveness analysis was conducted using a Markov model from the Canadian Public health (Ontario) perspective and a lifetime horizon in patients with stage IV NSCLC with nonsquamous histology. Transition probabilities and mortality rates were calculated from the Ontario Cancer Registry and Cancer Care Ontario New Drug Funding Program (CCO NDFP). Costs were obtained from the Ontario Case Costing Initiative, CCO NDFP, University Health Network, and literature. RESULTS: Molecular testing with first-line targeted crizotinib treatment in the population with advanced nonsquamous NSCLC resulted in a gain of 0.011 quality-adjusted life-years (QALYs) compared with standard care. The incremental cost was Canadian $2,725 per patient, and the incremental cost-effectiveness ratio (ICER) was $255,970 per QALY gained. Among patients with known EML4-ALK-positive advanced NSCLC, first-line crizotinib therapy provided 0.379 additional QALYs, cost an additional $95,043 compared with standard care, and produced an ICER of $250,632 per QALY gained. The major driver of cost effectiveness was drug price. CONCLUSION: EML4-ALK fusion testing in stage IV nonsquamous NSCLC with crizotinib treatment for ALK-positive patients is not cost effective in the setting of high drug costs and a low biomarker frequency in the population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".