Cost‐effectiveness analysis of trastuzumab in the adjuvant setting for treatment of HER2‐positive breast cancer
Bibliographic record
Abstract
BACKGROUND: Adding trastuzumab to adjuvant chemotherapy provides significant clinical benefit in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. A cost-effectiveness analysis was performed to assess clinical and economic implications of adding trastuzumab to adjuvant chemotherapy, based upon joint analysis of NSABP B-31 and NCCTG N9831 trials. METHODS: A Markov model with 4 health states was used to estimate the cost utility for a 50-year-old woman on the basis of trial results through 4 years and estimates of long-term recurrence and death based on a meta-analysis of trials. From 6 years onward, rates of recurrence and death were assumed to be the same in both trastuzumab and chemotherapy-only arms. Incremental costs were estimated for diagnostic and treatment-related costs. Analyses were from payer and societal perspectives, and these analyses were projected to lifetime and 20-year horizons. RESULTS: Over a lifetime, the projected cost of trastuzumab per quality-adjusted life year (QALY; discount rate 3%) gained was 26,417 dollars (range 9,104 dollars-69,340 dollars under multiway sensitivity analysis). Discounted incremental lifetime cost was 44,923 dollars, and projected life expectancy was 3 years longer for patients who received trastuzumab (19.4 years vs 16.4 years). During a 20-year horizon, the projected cost of adding trastuzumab to chemotherapy was 34,201 dollars per QALY gained. Key cost-effectiveness drivers were discount rate, trastuzumab price, and probability of metastasis. The cost-effectiveness result was robust to sensitivity analysis. CONCLUSIONS: Trastuzumab for adjuvant treatment of early stage breast cancer was projected to be cost effective over a lifetime horizon, achieving a cost-effectiveness ratio below that of many widely accepted oncology treatments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".