An exploratory case study of the impact of expanding cost-effectiveness analysis for second-line nivolumab for patients with squamous non-small cell lung cancer in Canada: Does it make a difference?
Bibliographic record
Abstract
INTRODUCTION: Health technology appraisal agencies often rely on cost-effectiveness analyses to inform coverage decisions for new treatments. These assessments, however, frequently measure a treatment's value from the payer's perspective, and may not capture value generated from reduced caregiving costs, increased productivity, value based on patient risk preferences, option value or the insurance value to non-patients. METHODS: To examine how using a broader societal perspective of treatment value affects cost-effectiveness estimates, this case study analyzed the net monetary benefit (NMB) of second-line nivolumab treatment of patients with squamous non-small cell lung cancer (NSCLC) in Canada. The comparator was treatment with docetaxel. NMB was measured from three perspectives: (i) traditional payer, (ii) traditional societal and (iii) broad societal. RESULTS: Nivolumab was more effective (increased quality-adjusted life years by 0.66 versus docetaxel), but also increased costs by $100,168 CAD. When valuing a quality-adjusted life year at $150,000, the net monetary benefit from the payer perspective suggested that costs modestly exceed benefits (NMB: -$1031). Adopting a societal perspective, however, nivolumab's benefits outweighed its costs (NMB: +$6752 and +$91,084 from the traditional and broad societal perspectives, respectively). CONCLUSION: Broadening cost-effectiveness analysis beyond the traditional payer perspective had a significant impact on the result and should be considered in order to capture all treatment benefits and costs of societal relevance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".