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A cost-utility analysis of apalutamide for the treatment of patients with metastatic castration-sensitive prostate cancer (mCSPC).

2020· article· en· W3031403900 on OpenAlexaffabout
Ambika Parmar, Narhari Timilshina, Urban Emmenegger, Shabbir M.H. Alibhai, Martin Smoragiewicz, Beate Sander, Kelvin Chan

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreSunnybrook HospitalUniversity of TorontoUniversity Health NetworkSunnybrook Health Science Centre
Fundersnot available
KeywordsEnzalutamideMedicineAndrogen deprivation therapyProstate cancerQuality-adjusted life yearCost effectivenessOncologyAdverse effectClinical trialInternal medicineCancerAndrogen receptor

Abstract

fetched live from OpenAlex

e19369 Background: The therapeutic landscape for patients with mCSPC has evolved over the last decade given the growing body of evidence demonstrating efficacy for the earlier application of oral androgen receptor signaling inhibitors, such as with abiraterone and enzalutamide. Recently the success of the TITAN trial established the superior efficacy of apalutamide combined with androgen deprivation therapy (ADT), with a 33% reduction in mortality versus ADT alone, and a tolerable toxicity profile. However, with substantially higher costs than ADT alone, the cost-effectiveness of this combination is critical to evaluate. Methods: A cost-utility analysis of apalutamide+ADT versus ADT alone was conducted from the Canadian healthcare perspective. A state-transition model with probabilistic analysis was used to compare the two strategies over a lifetime horizon. Model inputs were informed by the TITAN trial (transition probabilities, adverse events [AE], subsequent therapy), published literature (utilities) and Canadian costing resources (systemic therapy [initial and subsequent], routine care [physician visits, imaging], AE, end-of-life care costs). Primary outcomes included expected life-year gains (LYG), quality-adjusted life-years (QALY), lifetime cost (in 2018 Canadian dollars), and the incremental cost-effectiveness ratio (ICER). Multiple scenario analyses were conducted to assess parameter and model uncertainty. Cost and effectiveness were discounted at 1.5% as per Canadian guidelines. Results: From the base-case analysis expected LYG and QALY for ADT and apalutamide+ADT were 4.11, 5.57 and 3.50, 4.85, respectively. Expected cost over a lifetime horizon was $37,553 and $254,205, respectively. The ICER for apalutamide+ADT as compared to ADT alone was $160,483/QALY. Through scenario analysis, cost-effectiveness of apaluatmide+ADT was achieved with apalutamide price reductions of >50%, relative to a cost-effectiveness threshold (CET) of $100,000/QALY. Scenario analysis of alternative long-term survival expectations with apalutamide+ADT demonstrated cost-effectiveness (relative to CET $100,000/QALY) with expected improvements in the 5-year survival rate of 29% as compared to ADT (versus base-case expected improvement in 5-year survival of 15%). Conclusions: Apalutamide+ADT is unlikely to be cost-effective from the Canadian healthcare perspective at current list prices. Improvement in cost-effectiveness is most likely to be achieved through price reductions in apalutamide drug costs.

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)0.014
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 score0.244
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
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 head0.255
GPT teacher head0.518
Teacher spread0.263 · 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
Published2020
Admission routes2
Has abstractyes

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