MétaCan
Menu
← Back to cohort

Nivolumab in the treatment of metastatic renal cell carcinoma: A cost-utility analysis.

2017· article· en· W2891785187 on OpenAlexaffabout
Jacques Raphael, Zhuolu Sun, Georg A. Bjarnason, Beate Sander, David Naimark

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsSunnybrook HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsNivolumabMedicineEverolimusQuality-adjusted life yearRenal cell carcinomaOncologyCost–utility analysisQuality of life (healthcare)Cost effectivenessCohortInternal medicineClinical trialCost–benefit analysisCancerImmunotherapyRisk analysis (engineering)Nursing

Abstract

fetched live from OpenAlex

e18331 Background: Nivolumab was recently shown to improve overall survival (OS) and health-related quality of life compared to Everolimus in metastatic renal cell carcinoma (mRCC) patients previously treated with antiangiogenic therapies (CheckMate-025 trial). The aim of this study is to assess the cost-utility of Nivolumab versus Everolimus from the perspective of the Canadian publicly funded healthcare system. Methods: To evaluate the cost-utility of Nivolumab versus Everolimus, a Markov cohort model that incorporated data from the phase 3 CheckMate-025 trial and other sources was developed. The outcomes of interest were healthcare costs, life-months and quality-adjusted life-months (QALMs) gained with Nivolumab as well as the incremental cost-effectiveness ratio (ICER), and the incremental net monetary benefit. A lifetime time horizon was used in the base case with costs and outcomes discounted 5% annually. The probabilities of progression and death from cancer and utility values were captured from the CheckMate-025 trial. Expected costs were based on Ontario fees and other sources. Scenario and sensitivity analyses (SAs) were conducted to assess uncertainty. Results: Compared to Everolimus, treatment with Nivolumab provided an additional 3.9 QALMs at an incremental cost of 33,386 Canadian dollars (CAD). The resulting ICER was 8,608CAD per QALM gained. With a willingness-to-pay (WTP) of 50,000CAD per Quality-adjusted life-year (QALY) ( = 4,167CAD per QALM), Nivolumab was not cost-effective in the base case. In one-way SAs, Nivolumab cost, median OS and treatment duration on Nivolumab were sensitive to changes with plausible threshold values. Assuming a WTP of 100,000CAD per QALY ( = 8,334CAD per QALM) and a scenario of Nivolumab cost with no drug wastage, Nivolumab became a cost-effective strategy with an ICER of 7,881CAD per QALM. Conclusions: With its current price , Nivolumab is unlikely to be cost-effective compared with Everolimus for previously treated mRCC patients from a Canadian healthcare payer perspective. While mRCC patients derive a meaningful clinical benefit from Nivolumab, considerations should be given to reduce drug wastage and increase the WTP threshold to render this strategy more affordable.

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)0.006
metaresearch head score (Gemma)0.013
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 score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.239
GPT teacher head0.483
Teacher spread0.244 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicRenal cell carcinoma treatment→French-language works237,207→