Economic Evaluation of Hormonal Therapies for Postmenopausal Women with Estrogen Receptor–Positive Early Breast Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: Aromatase inhibitor (ai) therapy has been subjected to numerous cost-effectiveness analyses. However, with most ais having reached the end of patent protection and with maturation of the clinical trials data, a re-analysis of ai cost-effectiveness and a consideration of ai use as part of sequential therapy is desirable. Our objective was to assess the cost-effectiveness of the 5-year upfront and sequential tamoxifen (tam) and ai hormonal strategies currently used for treating patients with estrogen receptor (er)-positive early breast cancer. METHODS: The cost-effectiveness analysis used a Markov model that took a Canadian health system perspective with a lifetime time horizon. The base case involved 65-year-old women with er-positive early breast cancer. Probabilistic sensitivity analyses were used to incorporate parameter uncertainties. An expected-value-of-perfect-information test was performed to identify future research directions. Outcomes were quality-adjusted life-years (qalys) and costs. RESULTS: The sequential tam-ai strategy was less costly than the other strategies, but less effective than upfront ai and more effective than upfront tam. Upfront ai was more effective and less costly than upfront tam because of less breast cancer recurrence and differences in adverse events. In an exploratory analysis that included a sequential ai-tam strategy, ai-tam dominated based on small numerical differences unlikely to be clinically significant; that strategy was thus not used in the base-case analysis. CONCLUSIONS: In postmenopausal women with er-positive early breast cancer, strategies using ais appear to provide more benefit than strategies using tam alone. Among the ai-containing strategies, sequential strategies using tam and an ai appear to provide benefits similar to those provided by upfront ai, but at a lower cost.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".