Cost-Effectiveness of Taxanes in the Treatment of Metastatic Breast Cancer: A Review of the Literature
Bibliographic record
Abstract
Background/Aims: Metastatic breast cancer (MBC) is an incurable disease in which regardless of treatment, total survival time is often less than a year. In these patients suffering from MBC, mitigation of symptoms, reducing side effects of therapy and enhancing quality of life are the major issues in selecting a treatment. Taxanes have been shown to be efficacious as single agents or in combination with other agents as compared to other chemotherapy regimens. Docetaxel has received more support over paclitaxel in many analyses that have compared the two drugs indirectly. A direct comparison of both docetaxel and paclitaxel has not been possible until recently due to a lack of randomized trials. Little is known in reference to cost-effectiveness of taxanes in the treatment of MBC when compared directly. The objective of this literature review was to assess cost-effectiveness literature comparing docetaxel and paclitaxel in MBC. Methods: PubMed was used to conduct a search for manuscripts published 2005–2014. Search terms included “metastatic breast cancer” in addition to “cost-effectiveness” (n=96), “cost-utility” (n=9), “docetaxel” (n=332) and “paclitaxel” (n=336). Only articles that included cost-effectiveness analyses and a direct comparison among the taxanes were included. Results: A total of three cost-effectiveness studies were identified and included in the final review. Two of the three studies utilized data from a randomized controlled trial. The cost-effectiveness analyses reported incremental cost-effectiveness ratios (ICER) and quality adjusted life-years (QALY) and concluded that docetaxel, compared to paclitaxel, was superior and improved QALYs by 0.33–0.75. It is important to note that all three analyses were conducted outside of the United States. Discussion: In North America, breast cancer remains the most frequently diagnosed cancer in women. Yet, there are no new comprehensive economic analyses comparing the two taxanes published recently in the United States. Literature reflecting such comparisons has been published within the United Kingdom, Spain and Canada (reflected in this review). However, with the prevalence for the disease growing, it is important to continuously develop models assessing cost-effectiveness among patients with MBC.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".