Cost-Effectiveness of Osimertinib in Treating Newly Diagnosed, Advanced EGFR-Mutation-Positive Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess cost and effectiveness of osimertinib in treating newly diagnosed advanced non-small cell lung cancer with an epidermal growth factor receptor (EGFR) mutation from a public payer's perspective in the U.S. and China. MATERIALS AND METHODS: Markov models were developed to compare three treatment strategies: first-line use of osimertinib, first-line use of the standard first-generation EGFR-tyrosine kinase inhibitor (EGFR-TKI) followed by the second-line use of osimertinib, and the standard first-generation EGFR-TKI therapy (standard care [SOC]). Clinical data, cost, and utility data were mainly derived from published literatures. Deterministic and probabilistic sensitivity analyses were conducted to assess the robustness of the incremental cost per quality-adjusted life year (QALY) between the treatments. RESULTS: The resultant incremental cost per QALY gained for the first-line osimertinib versus SOC was $312,903 in the U.S. and $41,512 in China. The incremental cost per QALY for the second-line osimertinib versus SOC was $284,532 in the U.S. and $38,860 in China. The probability of the SOC strategy being cost-effective is 1.0 if the willingness to pay threshold is below $150,000/QALY in the U.S. and below $30,000/QALY in China. CONCLUSION: Osimertinib as first-line treatment could gain more health benefits in comparison with standard EGFR-TKIs or second-line use of osimertinib. However, because of the high cost of treatment, the cost-effectiveness analyses were not in favor of the first-line use of osimertinib from a public payer's perspective in the U.S. and China. IMPLICATIONS FOR PRACTICE: Osimertinib as first-line treatment yielded the greatest health outcomes but is not a cost-effective strategy for lung cancer in the U.S. and China. The price of osimertinib has a substantial impact on economic outcomes.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".