Cost-Effectiveness of Osimertinib in Treating Newly Diagnosed, Advanced EGFR-Mutation-Positive Non-Small Cell Lung Cancer
Why this work is in the frame
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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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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 it