Economic Analysis of the TAX 317 Trial: Docetaxel Versus Best Supportive Care as Second-Line Therapy of Advanced Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: To determine the cost-effectiveness (CE) of second-line docetaxel compared with best supportive care (BSC) in the TAX 317 trial, a randomized clinical trial of second-line chemotherapy in non–small-cell lung cancer. METHODS: A retrospective CE analysis of the TAX 317 trial was undertaken, evaluating direct medical costs of therapy from the viewpoint of Canada’s public health care system. Costs were derived in 1999 Canadian dollars, and resource use was determined through prospective trial data. RESULTS: The incremental survival benefit in the docetaxel arm over BSC was 2 months (P = .047). The CE of docetaxel was $57,749 per year of life gained. For patients treated with docetaxel 75 mg/m2, the CE was $31,776 per year of life gained. In univariate sensitivity analyses, CE estimates were most sensitive to changes in survival, ranging from $18,374 to $117,434 with 20% variation in survival at the recommended dose. The largest cost center in both arms was hospitalization, followed by the cost of drugs, investigations, radiotherapy, and community care. BSC patients had fewer hospitalizations than patients in the chemotherapy arm and were more often palliated at home. CONCLUSION: Although the decision to treat should not be based on economic considerations alone, our CE estimate of $31,776 per year of life gained (at the currently recommended dose of docetaxel) is within an acceptable range of health care expenditures, and the total costs of therapy are similar to those of second-line palliative chemotherapy for other solid tumors.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".