Clinical Effectiveness and Cost-Effectiveness of Multigene Panel Sequencing in Advanced Melanoma: A Population-Level Real-World Target Trial Emulation
Bibliographic record
Abstract
PURPOSE Targeted therapy and immunotherapy promise improved survival in patients with advanced melanoma, yet the effectiveness and cost-effectiveness of multigene panel sequencing compared with single-gene BRAF testing to guide therapeutic decisions is unknown. METHODS Our population-based quasi-experimental retrospective target trial emulation used comprehensive patient-level data for 364 British Columbia, Canada, adults with an advanced melanoma diagnosis receiving multigene panel sequencing or single-gene BRAF testing between September 1, 2016, and December 31, 2018. We 1:1 matched multigene panel patients to controls using genetic algorithm–based matching. Outcomes included 3-year overall survival (OS) and health care costs (2021 Canadian dollars [CAD]) with incremental net monetary benefit for life-years gained (LYG). Outcomes were analyzed using inverse probability of censoring weighted linear regression for the intention-to-treat (ITT) effect. The per-protocol (PP) effect estimation also included stabilized inverse probability of treatment weights. We then used Weibull regression and Kaplan-Meier survival analysis. RESULTS We matched 147 multigene panel patients to controls, achieving balance for all covariates. After matching, ITT incremental costs were $19,447 CAD (95% CI, -$19,045 to $50,683) and incremental LYG were 0.22 (95% CI, –0.05 to 0.49). We found uncertainty in differences on OS using Kaplan-Meier ( P = .11) and Weibull regression (hazard ratio [HR], 0.73 [95% CI, 0.51 to 1.03]) in the ITT. PP incremental costs were $25,457 CAD (95% CI, –$5,856 to $59,490) and incremental LYG were 0.39 (95% CI, 0.02 to 0.85), with corresponding differences in OS using Kaplan-Meier ( P = .06) and Weibull regression (HR, 0.62 [95% CI, 0.40 to 0.97]). The probability of multigene panels being cost-effective at $100,000/LYG CAD was 53% for ITT and 74% for PP. CONCLUSION The cost-effectiveness of multigene panels was evenly poised at higher thresholds, even when accounting for treatment initiation. Health systems reimbursing multigene panels and expensive therapies may be confronted with a value tradeoff, in which there may be improved survival albeit with a modest change in cost-effectiveness.
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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 | 0.052 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".