Lapatinib in patients with metastatic breast cancer following initial treatment with trastuzumab: an economic analysis from the Brazilian public health care perspective
Bibliographic record
Abstract
OBJECTIVE: To evaluate, from the perspective of the Brazilian public health care system, the cost-effectiveness of lapatinib plus capecitabine (LAP/CAP) versus capecitabine alone (CAP) or trastuzumab plus capecitabine (TRAST/CAP) in the treatment of women with human epidermal growth factor receptor-2-positive metastatic breast cancer previously treated with trastuzumab. METHODS: An economic model was developed to compare costs and clinical outcomes over a 5-year time horizon. Both costs and outcomes were discounted at a 5% rate, in accordance with Brazilian pharmacoeconomic guidelines. Clinical inputs were determined using indirect treatment comparisons. Costs were derived from public reimbursement databases and reported in 2010 Brazilian real (R$1 = USD$0.52). Clinical outcomes included progression-free survival years (PFYs), life-years (LYs) and quality-adjusted life-years (QALYs). The economic outcome was the incremental cost per LY, PFY, or QALY gained. The impact of variations in individual inputs (eg, drug cost, drug effectiveness) was examined using one-way sensitivity analyses. Overall model robustness was tested using probabilistic sensitivity analyses, varying the ranges of all input parameters within their standard distributions. RESULTS: Expected cost per patient was R$41,195 for CAP, R$95,256 for LAP/CAP, and R$113,686 for TRAST/CAP. Respective LYs were 1.406, 1.695, and 1.465; PFYs were 0.473, 0.711, and 0.612; and QALYS were 0.769, 0.958, and 0.827. LAP/CAP dominated TRAST/CAP for all outcomes. Incremental cost-effectiveness ratios of LAP/CAP over CAP were R$186,563 for LYs, R$226,403 for PFYs, and R$284,864 for QALYs. Results remained unchanged in one-way sensitivity analyses. In probabilistic analyses, LAP/CAP was dominant over TRAST/CAP in 93.5% of simulations. CONCLUSION: LAP/CAP increases survival for women with human epidermal growth factor receptor-2-positive metastatic breast cancer. LAP/CAP is cost-effective against TRAST/CAP (ie, produces more benefits at a lower cost) and can be considered cost-effective over CAP at a willingness-to-pay of about R$290,000 (US$151,000) per QALY gained.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".