Economic analysis of TORCH: Erlotinib versus cisplatin and gemcitabine as first-line therapy for advanced non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
e19031 Background: The TORCH (“Tarceva or Chemotherapy”) randomized phase III trial demonstrated that first-line erlotinib compared to cisplatin/gemcitabine in unselected advanced NSCLC patients yielded inferior survival, but no major differences in global quality of life. We determined the incremental costs and utility between arms, including in the EGFR mutation positive subgroup. Methods: Direct medical resource utilization data and EQ5D scores were collected prospectively during the trial. Costs for medications, outpatient visits, investigations and toxicity management including hospitalization were determined, and presented in 2012 Canadian dollars (CAD). The outcome of the analysis was the incremental cost per life year and quality-adjusted life-years (QALYs) gained. Results: The incremental mean cost per patient in the chemotherapy arm was $4,163CAD, largely related to drug and outpatient visit costs, while higher costs from hospitalization and adverse events were seen in the erlotinib arm. Mean overall and quality-adjusted survival times were longer in the chemotherapy arm. In the subset of patients with EGFR mutations (n=39), mean survival was not significantly different between arms (1.35 years for chemotherapy versus 1.29 years for erlotinib, p-value=0.86), but quality-adjusted survival favoured erlotinib treatment (i.e. mean QALYs were 1.04 for chemotherapy, and 1.40 for erlotinib). The incremental cost-effectiveness ratio for initial erlotinib in the EGFR mutation positive subgroup was $30,301 CAD per QALY. Conclusions: Initial chemotherapy in unselected advanced NSCLC yields better survival at minimal increased cost compared to erlotinib. In the EGFR mutation positive subgroup, first-line erlotinib is cost effective compared to first-line platinum doublet therapy, supporting routine EGFR genotyping to select first-line therapy in advanced NSCLC.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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) | 0.006 | 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".