Cost-effectiveness analysis of first-line treatment with crizotinib in ROS1-rearranged advanced non-small cell lung cancer (NSCLC) in Canada
Bibliographic record
Abstract
INTRODUCTION: While no direct comparative data exist for crizotinib in ROS1+ non-small cell lung cancer (NSCLC), studies have suggested clinical benefit with this targeted agent. The objective of this study was to assess the cost-effectiveness of crizotinib compared to standard platinum-doublet chemotherapy for first-line treatment of ROS1+ advanced NSCLC. METHODS: A Markov model was developed with a 10-year time horizon from the perspective of the Canadian publicly-funded health care system. Health states included progression-free survival (PFS), up to two further lines of therapy post-progression, palliation and death. Given a lack of comparative data and small study samples, crizotinib or chemotherapy studies with advanced ROS1+ NSCLC patients were identified and time-to-event data from digitized Kaplan-Meier curves were collected to pool PFS data. Costs of drugs, treatment administration, monitoring, adverse events and palliative care were included in 2018 Canadian dollars, with 1.5% discounting. An incremental cost-effectiveness ratio (ICER) was estimated probabilistically using 5000 simulations. RESULTS: In the base-case probabilistic analysis, crizotinib produced additional 0.885 life-years and 0.772 quality-adjusted life-years (QALYs) at an incremental cost of $238,077, producing an ICER of $273,286/QALY gained. No simulations were found to be cost-effective at a willingness-to-pay threshold of $100,000/QALY gained. A scenario analysis assuming efficacy equivalent to the ALK+ NSCLC population showed a slightly more favorable cost-effectiveness profile for crizotinib. CONCLUSIONS: Available data appear to support superior activity of crizotinib compared to chemotherapy in ROS1+ advanced NSCLC. At the list price, crizotinib was not cost-effective at commonly accepted willingness-to-pay thresholds across a wide range of sensitivity analyses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".