Cost-Effectiveness Analysis of Trastuzumab in the Adjuvant Treatment for Early Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Evidence from randomized controlled trials (RCTs) has shown a significant survival advantage of trastuzumab. Although extant work in developed countries examined economic evaluation of trastuzumab in adjuvant treatment for early breast cancer based on the 1-year treatment, there is uncertainty about cost-effectiveness of trastuzumab in the Adjuvant Treatment of early breast cancer in developing countries. This study aimed to estimate cost-effectiveness of adjuvant trastuzumab therapy compared to AC-T regimen in early breast cancer in Iran. METHODS: A cost-effectiveness analysis was performed using a Markov model to estimate outcomes and costs over a 20-year time period using a cohort of women with HER2 positive early breast cancer, treated with or without 12 months trastuzumab adjuvant chemotherapy. Transition probabilities were derived mainly from the BCIRG006 trial. Costs were estimated from the perspective of the Iranian health care system. Both costs and outcomes were discounted by 3%. One-way sensitivity analysis was undertaken to assess the associated uncertainties in the expected output measures. RESULTS: On the basis of BCIRG006 trial, our model showed that adjuvant trastuzumab treatment in early breast cancer, yield 0.87 quality-adjusted life-years (QALY) compared with AC-T regimen. Adjuvant trastuzumab treatment yielded an incremental cost-effectiveness ratio (ICER) of US$ 51302 per QALY. CONCLUSION: By using threshold of 3 times GDP per capita, as per World Health Organization (WHO) recommendation, 12 months trastuzumab adjuvant chemotherapy is not a cost-effective therapy for patients with HER2-positive breast cancer in Iran.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".