Review of cost-effectiveness studies on aromatase inhibitors for the treatment of early-stage breast cancer.
Bibliographic record
Abstract
e11030 Background: With the recent updates of clinical guidelines of the National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology (ASCO), aromatase inhibitors have been included in the management of early-stage breast cancer. There has been a great interest to understand the cost-effectiveness of this new alternative therapy which is becoming an optimal therapy for breast cancer. The objective of this study is to review the cost-utility studies on aromatase inhibitors for the treatment of early-stage breast cancer and compare reported incremental cost- effectiveness ratios (ICERs). Methods: We conducted a literature for cost-utility studies on anastrazole, letrozole and exemestane. We reviewed the papers to extract the information on intervention, comparator, ICER, country, perspective, time horizon and clinical data used. For the comparison of reported ICERs, we converted all currencies to U.S. dollars by exchange rate for the cost-year used, then inflated the values to 2008. Results: A total of 20 papers were identified (8 on anastrazole, 8 on letrozole and 4 on exemestane). All studies were from health care perspective and sponsored by manufacturers. The time horizon modeled ranged from 7.5 years to lifetime, however majority of the studies modeled lifetime. The studies were from EU countries and North America such as U.S., Canada, Belgium, Italy, Sweden and U.K. The mean ICER values were $24,932 for anastrazole, $21,113 for letrozole and $21,428 for exemestane. Conclusions: The mean ICERs for all three aromatase inhibitors are below $25,000; hence they appear to be cost-effective compared to tamoxifen therapy for the treatment of early-stage breast cancer. No significant financial relationships to disclose.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".