Decision Framework for Chemotherapeutic Interventions for Metastatic Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Best supportive care has long been considered to be the standard therapy for metastatic non-small-cell lung cancer (NSCLC). There is now evidence from randomized trials that a number of chemotherapy regimens can palliate cancer-related symptoms and modestly improve survival. We show how cost-effectiveness analyses can be used to make choices between different (ambulatory) chemotherapy regimens. METHODS: Clinical algorithms describing the diagnosis, staging, and treatment of metastatic NSCLC were incorporated into Statistics Canada's Population Health Model. Using consistent methodology, we assessed the cost-effectiveness of several chemotherapeutic interventions: a combination of vindesine (VDS) plus cisplatin, etoposide (VP-16) plus cisplatin, vinblastine (VLB) plus cisplatin, vinorelbine (Navelbine; NVB) plus cisplatin, paclitaxel (Taxol) plus cisplatin, and gemcitabine (GEM) and NVB alone. We calculated the total chemotherapy costs in 1995 Canadian dollars, the cost per case, the average life-years saved, and the cost per life-year saved. Using the Population Health Model, we then constructed an advanced decision framework that rank-ordered the various treatment regimens so as to optimize benefit below various cost-effectiveness thresholds. RESULTS: One regimen (VLB plus cisplatin) appears to result in better survival and lower health care expenditures than best supportive care. By use of cost-effectiveness thresholds of $25,000 and $50,000 per life-year gained, NVB plus cisplatin is the preferred regimen. When quality of life is considered, however, GEM is preferred to NVB plus cisplatin at a threshold value of $50,000. At thresholds of $75 000 and $100,000, paclitaxel plus cisplatin at a dose of 135 mg/m(2) is the preferred regimen. At thresholds of $50,000 and above, best supportive care is the least preferred regimen. CONCLUSIONS: This decision framework allows the comparison of different treatment regimens based on various cost-effectiveness thresholds. Our analysis also supports the use of chemotherapy regimens and the abandonment of best supportive care as the standard of care for patients with advanced NSCLC. [J Natl Cancer Inst 2000;92:1321-9].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".