Economic Evaluation of Adjuvant Trastuzumab Emtansine in Patients with HER2-Positive Early Breast Cancer and Residual Invasive Disease after Neoadjuvant Taxane and Trastuzumab–Based Treatment in Canada
Bibliographic record
Abstract
Background: < 0.0001) in patients with her2-positive early breast cancer (ebc) and residual invasive disease after neoadjuvant systemic treatment. A cost-utility evaluation, with probabilistic analyses, was conducted to examine the incremental cost per quality-adjusted life-year (qaly) gained associated with T-DM1 relative to trastuzumab, given the higher per-cycle cost of T-DM1. Methods: A Markov model comprising a number of health states was used to examine clinical and economic outcomes over a lifetime horizon from the Canadian public payer perspective. Patients entered the model in the invasive disease-free survival (idfs) state, where they received either T-DM1 or trastuzumab. Transition probabilities between the health states were derived from the katherine trial, Canadian life tables, and published literature from other relevant clinical trials (emilia, cleopatra, and M77001). Resource use, costs, and utilities were derived from katherine, other clinical trials, published literature, provincial fee schedules, and clinical expert opinion. Sensitivity analyses were conducted for key assumptions and model parameters. Results: Compared with trastuzumab, adjuvant T-DM1 was associated with a cost savings of $8,300 per patient and a 2.16 incremental qaly gain; thus T-DM1 dominated trastuzumab. Scenario analyses yielded similar results, with T-DM1 dominating trastuzumab or producing highly favourable incremental cost-utility ratios of less than $10,000 per qaly. Conclusions: Adjuvant T-DM1 monotherapy is a cost-effective strategy compared with trastuzumab alone in the treatment of patients with her2-positive ebc and residual invasive disease after neoadjuvant systemic treatment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".