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Cost-effectiveness of dabrafenib plus trametinib in BRAFV600E-mutant pediatric low-grade glioma: A microsimulation study.

2024· article· en· W4399672006 on OpenAlexaffabout
Bryan Gascon, Alan Yang, Cindy L. Gauvreau, Julie Bennett, Liana Nobre, Uri Tabori, Cynthia Hawkins, Petros Pechlivanoglou, Avram Denburg

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsSickKids FoundationUniversity of TorontoUniversity of AlbertaHospital for Sick Children
Fundersnot available
KeywordsTrametinibMedicineDabrafenibGliomaMutantOncologyInternal medicineCancer researchCancerGeneticsGeneMAPK/ERK pathwayBiologyPhosphorylationVemurafenibMetastatic melanoma

Abstract

fetched live from OpenAlex

2055 Background: BRAFV600E mutations, detected in 15-20% of pediatric low-grade gliomas (PLGGs), have emerged as a key therapeutic target for patients with unresectable, progressive, or recurrent PLGG. In a recent phase II clinical trial (NCT02684058), dabrafenib, a selective inhibitor targeting BRAFV600E, plus trametinib outperformed standard chemotherapy as first-line therapy, demonstrating significantly longer progression-free survival and improved safety in BRAFV600E-mutant PLGG. The aim of this study was to estimate the cost-effectiveness of dabrafenib+trametinib (Dab-Tram) versus standard chemotherapy as first-line therapy for patients with BRAFV600E-mutant PLGG. Methods: We constructed a microsimulation model, simulating a cohort of 10,000 patients with BRAFV600E PLGG. We populated the model using progression-free and overall survival estimates derived from the NCT02684058 trial, a hospital-based PLGG registry and treatment- and PLGG-related adverse events from published literature. The simulated cohort was assigned to either targeted therapy or chemotherapy as first-line therapy. Key parameters included treatment cost, dosage, inpatient and outpatient costs, and health-related quality of life. We assumed a lifetime duration of targeted therapy, a healthcare system perspective, lifetime horizon, and discount rate of 1.5%. Outcomes included life years, quality-adjusted life years (QALYs), lifetime costs (2022 CAD), and incremental cost-effectiveness ratios (ICERs).Sensitivity analysis included the use of alternative data sources, including those derived from independent assessment of NCT02684058 trial outcomes and real-world data from The Hospital for Sick Children (SickKids) in Toronto, Canada. Results: Modeled life years with Dab-Tram and chemotherapy were 34.91 and 32.68 years, respectively. Dab-Tram was associated with a 1.77 QALY increase at an incremental cost of $2,701,409 compared to standard chemotherapy. The resulting ICER was $1,526,265 per QALY gained. At a willingness-to-pay (WTP) threshold of $150,000 per QALY, a price reduction of approximately 80% is required to render it cost-effective. Scenario analysis varying effectiveness of targeted therapy using independent assessment of NCT02684058 trial outcomes and real-world data from SickKids demonstrated comparable clinical benefits and ICERs. Results were sensitive to changes in survival distributions. Conclusions: While clinical outcomes using Dab-Tram as first line therapy for BRAFV600E PLGG are promising, our model-based analysis demonstrates that at the present price, Dab-Tram is not cost-effective, and would require a considerable price reduction to enable equitable access and affordability. This work also provides an exemplar for economic evaluation of precision therapies based on emergent trial and real-world data for rare diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.205
GPT teacher head0.543
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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