Adjuvant hormone strategies in postmenopausal women with breast cancer: An economic evaluation based on the EBCTCG meta-analyses
Bibliographic record
Abstract
6574 Background: Meta-analyses conducted by the Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) demonstrated significant improvements in breast cancer (BC) outcomes associated with tamoxifen (TAM) and/or aromatase inhibitor (AI) therapy. We conducted an economic evaluation, based on these meta-analyses, to examine the cost-effectiveness (CE) of adjuvant TAM, sequential TAM-AI and upfront AI in post menopausal (PM) women with BC. Methods: A generic model was developed to calculate cumulative costs and quality adjusted life year gains (QALYG) over 25-year horizon in hypothetical cohorts of PM women with BC undergoing 5-year treatment with TAM alone, upfront AI, or sequential TAM-AI. We examined different cohorts with varying 10-year baseline recurrence risk (RR) without adjuvant hormonal treatments to reflect the natural spectrum of breast cancer disease encountered (low = 25%; average = 38%; high = 50%; very high = 75%). The efficacy outcomes were derived from the EBCTCG meta-analyses, with 10-year duration of benefit assumed for all strategies. Costs and utilities were derived from the literature, and local resources. The analysis took a direct payer perspective and reports costs in 2008 Cdn$. Costs and benefits were discounted at 3%. CE was based on the $50,000/QALY gained threshold. Adverse events were not included in the primary analysis. Sensitivity analyses were conducted. Results: Adjuvant hormonal treatments with TAM and/or AI are CE strategies in PM women with BC. The costs and QALYG associated with hormonal treatments were dependent on the baseline RR: CE estimates were more favorable in cohorts with higher as opposed to lower RR. The baseline RR also influenced the choice of the optimal economic strategy. Upfront AI was associated with higher costs and more QALY gains compared to TAM-AI. CE however was favorable in patients with average to very high RR and unfavorable in patients with low RR. Conclusions: Adjuvant treatments with TAM and/or AI are associated with favorable CE in PM women with BC. The optimal CE strategies, however, are dependent on the baseline RR without hormonal treatment. A risk-tailored hormonal treatment choice could optimize the overall health system efficiency. [Table: see text]
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".