Real-world cost-effectiveness of multi-gene panel sequencing to inform therapeutic decisions for advanced non-small cell lung cancer: a population-based study
Bibliographic record
Abstract
Background: Multi-gene panel sequencing streamlines treatment selection for advanced non-small cell lung cancer (NSCLC). Implementation continues to be uneven across jurisdictions, partly due to uncertain clinical and economic impacts. In British Columbia (BC), Canada, the public healthcare system reimbursed a multi-gene panel in September 2016. This study determined the population-level cost-effectiveness of publicly reimbursed multi-gene panel sequencing compared to single-gene testing for advanced NSCLC. Methods: Our population-based retrospective study design used patient-level linked administrative health databases. We considered adult BC residents with a panel-eligible lung cancer diagnosis between September 2016 and December 2018. Using a machine learning approach, we conducted 1:1 genetic algorithm matching of recipients receiving multi-gene panel sequencing to controls receiving single-gene testing, maximising balance on observed demographic and clinical characteristics. Following matching, we estimated mean three-year survival time and costs (public healthcare payer perspective; 2021 CAD) and calculated the incremental net monetary benefit (INMB) for life-years gained (LYG) at conventional willingness-to-pay thresholds using inverse probability of censoring weighted linear regression and nonparametric bootstrapping. Findings: We matched 858 panel-eligible advanced NSCLC patients to controls, achieving balance for the 16 included covariates. Average test turnaround times were 18.6 days for multi-gene panel sequencing and 7.0 days for single-gene testing. After matching, mean incremental costs were $3529 (95% CI: -$4268, $10,942) and mean incremental LYG were 0.08 (95% CI: -0.04, 0.18). Among the 1000 bootstrap samples, 14.5% had lower costs and increased survival and 78.6% had higher costs and increased survival. The INMB was $523 (95% CI: -$6256, $7023) at $50,000/LYG, with a 57.5% probability of being cost-effective, and $4575 (95% CI: -$5468, $14,064) at $100,000/LYG, with an 84.0% probability of being cost-effective. Interpretation: Using population-based real-world data, we found a moderate to high probability that panel-based testing to inform targeted treatment for NSCLC would be cost-effective at higher thresholds. Funding: This research was supported by Genome British Columbia/Genome Canada (G05CHS) and the Terry Fox Research Institute.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".