ArticleHeliyon2024
Cost-effectiveness of multigene sequencing test and treatment for metastatic non-small cell lung cancer: A unique setting in the initial adoption phase in Japan allowing testing only after standard treatment.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Economic evaluations of next-generation sequencing for targeted therapy in non-small cell lung cancer: a systematic review.Frontiers in public health · 2026Pooled it
- Real-World Testing Landscape and Costs of Companion Diagnostics and Comprehensive Genomic Profiling Across Nine Solid Tumors in Japan: A 10-Year Analysis.Oncology and therapy · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To clarify the cost-effectiveness of comprehensive diagnosis and treatment of metastatic non-small cell lung cancer in Japan, from initial diagnosis to post-standard treatment, using three different strategies. Methods: A decision tree was created using three diagnostic and treatment strategies, assuming that Foundation One CDx (F1CDx), a comprehensive genome panel, was introduced in Japan in June 2019. This comprehensive decision tree includes Markov models, cost-effectiveness analyses (CEA), and cost-utility analyses (CUA) of the three strategies from the perspective of Japanese payers. Specifically, Strategy1 involves single-gene testing at the initial diagnosis and F1CDx after standard treatment; Strategy2 involves only single-gene testing at the initial diagnosis; Strategy3 involves F1CDx at the initial diagnosis. The incremental cost-effectiveness ratios (ICERs) of the three strategies are estimated. Sensitivity analyses were performed to assess the uncertainty of the parameter settings. Results: Strategy3 was dominated for both CUA and CEA. The ICER/quality-adjusted life year (QALY) for Strategy2 versus Strategy1 was USD 13,734 (JPY 2,080,923, USD 1 = JPY 151.39 on April 1st, 2024), which is less than the willingness to pay of USD 45,900 (JPY 7,500,000), and Strategy2 was more cost-effective than Strategy1. F1CDx was not cost-effective compared to multiple simultaneous single tests at the initial diagnosis, either after standard treatment or at the initial diagnosis. Sensitivity analysis also showed that the most influential factor on the ICER for both CEA and CUA was treatment cost. Conclusions: From both patient benefit and health economic perspectives, introducing F1CDx after standard treatment in June 2019 was not as cost-effective as multiple simultaneous single tests at the initial diagnosis but was more realistic from a health economic perspective than introducing F1CDx at the time of initial diagnosis. Therefore, the policy at the time of F1CDx introduction in Japan was appropriate from a short-term health-economic perspective.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.