Cost-Effectiveness of Adjuvant Alectinib Versus Chemotherapy for Patients with Resectable, ALK-positive Non-small Cell Lung Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: For patients with resected non-small cell lung cancer (NSCLC), the risk of disease recurrence and progression is associated with a substantial humanistic, clinical, and economic burden. In the phase III ALINA trial (NCT03456076), adjuvant alectinib significantly improved disease-free survival (DFS) compared with chemotherapy in patients with resected ALK-positive NSCLC. OBJECTIVE: The aim of this study was to assess the cost-effectiveness of adjuvant alectinib versus chemotherapy for patients with resected ALK-positive NSCLC in Canada. METHODS: A cost-utility model comprising eight health states was developed to estimate lifetime patient outcomes and costs of patients with resected, ALK-positive NSCLC treated with adjuvant alectinib versus platinum-based chemotherapy from a societal perspective. Patterns of disease recurrence and progression were based on ALINA and other trial data; model assumptions were consistent with existing models and validated through consultation with expert Canadian clinicians. Cost-effectiveness was assessed in terms of estimated effect on life-years, quality-adjusted life-years (QALYs), and healthcare costs. In addition, scenario and probabilistic analyses were performed to explore model uncertainty. An annual discount rate of 1.5% was applied to both costs and outcomes (evaluated for 2023). RESULTS: Compared with chemotherapy, alectinib was associated with greater total life-years (19.2 versus 13.1 years) and QALYs (15.0 versus 10.1). Alectinib was dominant over platinum-based chemotherapy as it yielded a lower lifetime cost (CA $480,967.00) versus chemotherapy (CA $592,959.00). Scenario analyses showed model robustness and consistent dominance in cost-effectiveness. Probabilistic analyses results were similar to those from the base case and scenario analyses; alectinib was dominant over chemotherapy in 93.6% of simulations of incremental costs versus incremental QALYs, and remained under a willingness-to-pay threshold of CA$50,000.00 per QALY gained in 99.7% of simulations. CONCLUSIONS: Our analysis suggests that adjuvant alectinib is dominant (i.e., more effective and less costly) to platinum-based chemotherapy in Canadian patients with resected ALK-positive NSCLC. Together with the DFS benefit seen in ALINA, this analysis supports adjuvant alectinib as an important new treatment strategy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".