Nab-paclitaxel, docetaxel, or solvent-based paclitaxel in metastatic breast cancer: a cost-utility analysis from a Chinese health care perspective
Bibliographic record
Abstract
BACKGROUND: Paclitaxel and docetaxel are commonly used for metastatic breast cancer in the People's Republic of China. To improve the safety and efficacy of paclitaxel, an albumin-bound formulation (nab) is now available in the People's Republic of China (Abraxane(®)). To provide health economic data for the key stakeholders, a cost-utility analysis comparing nab-paclitaxel to docetaxel, both as alternatives to paclitaxel, was conducted. METHODS: A meta-analysis of clinical outcomes Phase III trials comparing nab-paclitaxel (260 mg/m(2) every [q] 3 weeks) or branded docetaxel (100 mg/m(2) q 3 weeks), to solvent-based branded paclitaxel (175 mg/m(2) q 3 weeks) was undertaken to provide safety and clinical data. Resource use data for the delivery of anticancer therapy and for the treatment of grade 3/4 toxicity was collected from a time and motion study conducted in three Chinese cancer centers and from a survey of clinicians. Using the Time Trade-Off technique, health utility estimates were derived from interviewing 28 breast cancer patients from one cancer center in the People's Republic of China. All costs were reported in 2014 US dollars. RESULTS: Nab-paclitaxel had the most favorable safety profile, characterized with the lowest incidence of grade 3/4 neutropenia, febrile neutropenia, anemia, and stomatitis. When the median number of cycles delivered from the clinical trials was applied, nab-paclitaxel had a cost per course of $19,752 compared with $8,940 and $13,741 for paclitaxel and docetaxel, respectively. As an alternative to paclitaxel, the cost per quality-adjusted life-year (QALY) gained with nab-paclitaxel suggested better value than with docetaxel ($57,900 vs $130,600). CONCLUSION: Nab-paclitaxel appears to be a cost-effective option compared with docetaxel and paclitaxel, for metastatic breast cancer in the People's Republic of China.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".