Cost-Effectiveness Analysis of Durvalumab Plus Chemotherapy in the First-Line Treatment of Extensive-Stage Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: In the CASPIAN trial, durvalumab + chemotherapy demonstrated significant improvements in overall survival compared with chemotherapy alone in patients with extensive-stage small cell lung cancer (SCLC). We aimed to assess the cost-effectiveness of durvalumab in patients with extensive-stage SCLC from the US healthcare system perspective. PATIENTS AND METHODS: A comprehensive Markov model was adapted to evaluate cost and effectiveness of durvalumab combination versus platinum/etoposide alone in the first-line therapy of extensive-stage SCLC based on data from the CASPIAN study. The main endpoints included total costs, life years (LYs), quality-adjusted life-years (QALYs), and incremental cost-e-ectiveness ratios (ICERs). Model robustness was assessed with sensitivity analysis, and additional subgroup analyses were also performed. RESULTS: Durvalumab + chemotherapy therapy resulted in an additional 0.27 LYs and 0.20 QALYs, resulting in an ICER of $464,711.90 per QALY versus the chemotherapy treatment. The cost of durvalumab has the greatest influence on this model. Subgroup analyses showed that the ICER remained higher than $150,000/QALY (the willingness-to-pay threshold in the United States) across all patient subgroups. CONCLUSIONS: Durvalumab in combination with platinum/etoposide is not a cost-effective option in the first-line treatment of patients with extensive-stage SCLC.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| 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".