Cost-Effectiveness Analysis Of EGFR Mutation Testing And Afatinib Versus Gemcitabine-Cisplatin As First-Line Therapy For Advanced Non-Small-Cell Lung Cancer In China.

Afatinib EGER mutation testing Economic analysis NSCLC incremental cost-effectiveness ratio

Journal

Cancer management and research
ISSN: 1179-1322
Titre abrégé: Cancer Manag Res
Pays: New Zealand
ID NLM: 101512700

Informations de publication

Date de publication:
2019
Historique:
received: 17 06 2019
accepted: 09 10 2019
entrez: 12 12 2019
pubmed: 12 12 2019
medline: 12 12 2019
Statut: epublish

Résumé

The purpose of this study was to evaluate the cost-effectiveness of the combined use of afatinib and epidermal growth factor receptor (EGFR) testing versus gemcitabine-cisplatin as the first-line treatment for patients with non-small cell lung cancer (NSCLC) in China. A decision-analytic model, based on clinical phase III trials, was developed to simulate patient transitions. Direct costs were estimated from the perspective of the Chinese healthcare system. Quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICER) were calculated over a 5-year lifetime horizon. Model robustness was conducted in sensitivity analyses. For the base case, EGFR mutation testing followed by afatinib treatment for advanced NSCLC increased 0.15 QALYs compared with standard chemotherapy at an additional cost of $5069.12. The ICER for afatinib maintenance was $33,416.39 per QALY gained. The utility of PFS and the cost of afatinib had the most important impact on the ICER. Scenario analyses suggested that when a patient assistance program (PAP) was available, ICER decreased to $22,972.52/QALY lower than the willingness-to-pay (WTP) threshold of China ($26,508/QALY). Our results suggest that gene-guided maintenance therapy with afatinib with the PAP might be a cost-effective treatment option compared with gemcitabine - cisplatin in China.

Identifiants

pubmed: 31824194
doi: 10.2147/CMAR.S219722
pii: 219722
pmc: PMC6901062
doi:

Types de publication

Journal Article

Langues

eng

Pagination

10239-10248

Informations de copyright

© 2019 You et al.

Déclaration de conflit d'intérêts

The authors report no conflicts of interest in this work.

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Auteurs

Ruxu You (R)

Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.

Jinyu Liu (J)

Department of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.

David Bin-Chia Wu (DB)

School of Pharmacy, Monash University Malaysia, Kuala Selangor, Malaysia.

XinYu Qian (X)

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

Boxiang Lyu (B)

Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Yu Zhang (Y)

Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.

Nan Luo (N)

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

Classifications MeSH