Cost-effectiveness analysis (CEA) of third-line erlotinib therapy compared to best supportive care for advanced non-small cell lung cancer (NSCLC) in British Columbia (BC)
Bibliographic record
Abstract
7678 Background: Erlotinib was approved for funding as a systemic therapy treatment for 3rd line management of advanced NSCLC by the BC Cancer Agency (BCCA) in April 2004. BCCA patient outcome and cost data are routinely collected to verify the therapeutic effectiveness and cost-effectiveness of systemic treatment policies. Methods: This was a pragmatic retrospective analysis of all patients who received 3rd line erlotinib compared to a historical group treated with 2nd-line docetaxel then no further active treatment, both according to BCCA protocol. The primary end-point was cost-effectiveness, measured in terms of cost per-life-year-gained. Secondary end-points included: median overall survival (MOS); overall survival (OS) at 1 year; and comparison to phase III efficacy results. Data was retrieved from the Cancer Agency Information System (CAIS) and Systemic Therapy Data Warehouse. Life-years- gained were calculated from the area under the survival function curve. CEA took the BCCA perspective and costs included all direct drug costs for treatment of advanced disease. Sensitivity analyses included varying life expectancy across its 95% CI, cost to the extremes of ranges, and start date for length of survival: method 1. progression after 2nd line therapy or 3 weeks post last dose of chemotherapy for control group and start date of erlotinib for treatment group; method 2. last date of 2nd line therapy for both groups Results: 75 control and 70 erlotinib patients were included in the analysis. Results are presented in the table . The Incremental Cost-Effectiveness Ratio (ICER) was $28,516 per life-year-gained under method 1, and $17,632 under method 2. The erlotinib group had similar 1-year OS compared to literature (36 vs. 31%). Conclusions: Erlotinib appears to be cost-effective in terms of life-expectancy under a range of assumptions. No significant financial relationships to disclose. [Table: see text]
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".