Cost-Effectiveness Analysis of Recurrence Score-Guided Treatment Using a 21-Gene Assay in Early Breast Cancer
Bibliographic record
Abstract
PURPOSE: Most guidelines for hormone receptor (HR)-positive early breast cancer recommend addition of adjuvant chemotherapy for most women, leading to overtreatment, which causes considerable morbidity and cost. There has been recent incorporation of gene expression analysis in aiding decision making. We evaluated the cost-effectiveness of recurrence score (RS)-guided treatment using 21-gene assay as compared with treatment guided by the Adjuvant! Online program (AOL). PATIENTS AND METHODS: A Markov model was developed to compare the cost-effectiveness of treatment guided either by 21-gene assay or by AOL in a 50-year-old woman with lymph node-negative HR-positive breast cancer over a lifetime horizon. We assumed that women classified to be at high risk all received chemotherapy followed by tamoxifen and those classified to be at low risk received tamoxifen only. The model took a health care payer's perspective with results reported in 2008 Canadian dollars ($). Event rates, costs, and utilities were derived from the literature. Both costs and benefits were discounted at 5%. Outcome measures were life years gained, quality-adjusted life years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs). RESULTS: For a 50-year-old woman, RS-guided treatment was associated with an incremental lifetime cost of $4,102 and a gain in 0.065 QALY, with an ICER of $63,064 per QALY compared with AOL-guided treatment. ICER increased with increasing cost of 21-gene assay and increasing age of patients. Results were most sensitive to probabilities relating to risk categorization and recurrence rate. CONCLUSIONS: The 21-gene assay appears cost-effective from a Canadian health care perspective.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".