Cost-effectiveness analysis of oncotype DX-guided treatment in early breast cancer
Bibliographic record
Abstract
e11536 Background: Some early breast cancer patients with hormone receptor positive (HR+) disease may benefit from adjuvant chemotherapy in addition to hormonal therapy. Most guidelines recommend the addition of adjuvant chemotherapy for majority of women, leading to overtreatment causing considerable morbidity and cost. There has been recent incorporation of gene expression analysis to help aid in adjuvant chemotherapy decision making. We evaluated the cost-effectiveness of Oncotype DX-guided treatment as compared to treatment guided by Adjuvant! Online program. Methods: A Markov model was developed to compare the cost- effectiveness of treatment guided by either Oncotype DX or Adjuvant! Online in a cohort of 50-year-old women with HR+, lymph node negative breast cancer over a lifetime horizon. We assumed women assessed to be high risk all received chemotherapy followed by tamoxifen, and that those assessed to be 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%. Sensitivity analysis for key parameters in the model was conducted. Outcome measures were life year gained, quality- adjusted life years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs). Results: For a 50-year-old woman, Oncotype DX-guided treatment was associated with an incremental lifetime cost of $4,200 and a gain in 0.067 QALY, with an ICER of $61,800/QALY compared with treatment guided by Adjuvant! ICER was positively correlated to the cost of Oncotype DX and the age of patients. Results were most sensitive to probabilities relating to risk categorization and recurrence rate. Conclusions: Relative to Adjuvant!-guided treatment, Oncotype DX-guided treatment appears cost-effective with an ICER of $61,800/QALY from a Canadian health care perspective. [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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".