OP61 Target Trial Emulation To Determine The Population-Level Cost-Effectiveness Of Multigene Panel Sequencing For Advanced Melanoma
Bibliographic record
Abstract
Introduction Compared to single-gene BRAF testing to guide targeted treatment with BRAF/MEK inhibitors for advanced melanoma, multigene panels can identify additional gene mutations with known therapeutic or prognostic relevance. Implementation of multigene panels remains uneven across healthcare systems given an uncertain clinical and economic evidence base. We determined the population-level cost-effectiveness of multigene panels compared to single-gene BRAF testing for advanced melanoma. Methods Our population-based retrospective study emulated a hypothetical pragmatic trial comparing multigene panel sequencing to single-gene BRAF testing. We drew on comprehensive patient-level clinical and health administrative data between September 2016 and December 2018 in British Columbia, Canada. To emulate random treatment assignment, we 1:1 matched multigene panel patients to contemporaneous single-gene tested controls using genetic algorithm-based matching. We estimated three-year overall survival and healthcare costs (2021 CAD), and incremental net monetary benefit (INMB) for life years gained (LYG) using inverse probability of censoring weighted linear regression and nonparametric bootstrapping. We also estimated overall survival using Weibull regression and Kaplan–Meier survival analysis. Results We matched 147 patients with advanced melanoma receiving multigene panel sequencing to contemporaneous single-gene-tested controls, achieving good balance for all 15 baseline clinical and sociodemographic covariates. After matching, mean incremental costs were CAD19,447 (USD14,217) (95% confidence interval [CI]: −CAD18,517 [−USD13,537], CAD76,006 [USD55,565]; p=0.41) and mean incremental LYG were 0.22 (95% CI: −0.05, 0.49; p=0.12). We found uncertain differences on overall survival using Kaplan–Meier (stratified Log-rank test p=0.11) and Weibull regression (HR: 0.73 [95% CI: 0.51, 1.03]; p=0.07) survival analysis. Cost differences were driven by systemic therapy (∆C: CAD8,665 [USD6,334]; 95% CI: −CAD36,387 [−USD26,600], CAD53,716 [USD39,268]; p=0.71). The INMB at CAD100,000(USD73,104)/LYG was CAD2,646 (USD1,934) (95% CI: −CAD30,044 [−USD21,963], CAD43,416 [USD31,739]; p=0.89), with a 52.8 percent probability of being cost effective. Conclusions There were clinically relevant but uncertain differences in improved survival associated with multigene panel sequencing for advanced melanoma, and the cost-effectiveness of panel-based testing was finely balanced. This real-world evidence generated using randomized trial design principles can support jurisdictions’ deliberations on the reimbursement of precision oncology interventions.
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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 | 0.042 | 0.060 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".