Cost-Effectiveness of Pyrotinib Plus Capecitabine versus Lapatinib Plus Capecitabine for the Treatment of HER2-Positive Metastatic Breast Cancer in China: A Scenario Analysis of Health Insurance Coverage
Bibliographic record
Abstract
BACKGROUND: The overexpression of the human epidermal growth factor receptor-2 (HER2) gene is present in 20~25% of breast cancer (BC) patients, contributing to an inferior prognosis. Recent clinical trials showed that pyrotinib has promising antitumor activities and acceptable tolerability for those patients (ClinicalTrials.gov, NCT03080805 and NCT02422199). Therefore, this study aims to assess the cost-effectiveness of pyrotinib plus capecitabine versus lapatinib plus capecitabine for patients with HER2-positive metastatic BC after prior trastuzumab. METHODS: A lifetime-partitioned survival model was established to evaluate health and economic outcomes with different treatment strategies. The primary outcome was the incremental cost-effectiveness ratio (ICER). Data were derived from the published literature, clinical trials, expert opinions, and other local charges. Sensitivity analyses were performed to assess the robustness of the findings. Scenario analyses were developed to make further evaluations. RESULTS: The pyrotinib regimen had significant advantages over the lapatinib regimen after enrolling in the National Reimbursement Drug List (NRDL), with cost savings of USD 15,599.27 and a gain of 0.53 QALYs. Meanwhile, before enrolling in NRDL, the pyrotinib regimen afforded the same QALYs at a higher incremental cost of USD 45,400.64 versus the lapatinib regimen, producing an ICER of USD 85,944.79 per QALY. Scenario analyses yielded similar results. Sensitivity analyses suggested stability in the cost-effectiveness findings. CONCLUSIONS: Compared to lapatinib plus capecitabine, the pyrotinib plus capecitabine enrolled in NRDL is a cost-effective alternative second-line treatment for patients with HER2-positive metastatic BC in China.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".