Cost effectiveness of ceritinib in patients previously treated with crizotinib and chemotherapy with anaplastic lymphoma kinase positive (ALK+) non-small cell lung cancer (NSCLC) in the United States
Bibliographic record
Abstract
Abstract BackgroundLung cancer is the leading cause of cancer death in the United States. Among non-small cell lung cancer (NSCLC) patients with anaplastic lymphoma kinase mutation (ALK+) and who were resistant to crizotinib, ceritinib was approved as a following treatment. Ceritinib was found to be cost effective among Canadian patients, but its cost effectiveness among US population remains unknown.ObjectiveTo evaluate the cost-effectiveness of ceritinib versus chemotherapy among ALK+ NSCLC patients who received treatment of crizotinib and chemotherapy, from the US healthcare perspective.MethodsA Markov model with three health states (progression-free, progression, and death) and a partitioned survival analysis model (PartSA) were developed, respectively. Survival functions, including progression free survival and overall survival, for ceritinib and chemotherapy were extrapolated from clinical trials ASCEND-2, ASCEND-5, and PROFILE-1007. Costs for the drugs, monitoring, and adverse events, and utilities at each health state were derived from published literature. Costs were inflated to 2018 US dollars. An annual discount rate of 3% was applied to costs and utilities, and a 5-year time horizon was applied to the analysis. Incremental cost per quality-adjusted life year (QALY) gained for ceritinib versus chemotherapy was estimated with $150,000/QALY as the US willingness-to-pay threshold. Sensitivity analyses (i.e., one-way sensitivity analysis and probabilistic sensitivity analysis) were conducted to test the uncertainties of the models.ResultsBoth models find ceritinib yields fewer QALYs than chemotherapy. The Markov model indicates modest cost-savings of ceritinib when compared to chemotherapy (-$3,131) and small declines in health (-1.04 QALYs) ($3008.39 per QALY lost). The PartSA model indicates additional costs of ceritinib when compared to chemotherapy ($12,884.95) and similar declines in health (-0.87 QALYs), indicating a dominated strategy. Both models were most sensitive to parameters of medical cost for progression disease and cost of ceritinib.ConclusionsCeritinib is not cost effective compared to chemotherapy among patients who were previously treated with crizotinib and chemotherapy, from the US healthcare perspective.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".