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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".