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. 2025 Jun 16;15(1):50.
doi: 10.1186/s13561-025-00651-6.

How was published evidence used in model-based cost - utility analysis for lung cancer?

Haijing Guan #  1   2 , Chunping Wang #  3   4 , Ruowei Xiao  3   4 , Ting Zhou  5 , Wei Li  3   4   6   7 , Yanan Xu  1   8 , Hongting Nie  9 , Zhigang Zhao  1 , Sheng Han  10   11 , Feng Xie  12
Affiliations

How was published evidence used in model-based cost - utility analysis for lung cancer?

Haijing Guan et al. Health Econ Rev. .

Abstract

Background: Model-based cost-utility analysis (CUA) is a widely used method for evaluating the value of innovative medicines for lung cancer. However, comprehensive evidence exploring the sources of input parameters for CUA modeling is lacking. The objective of this study was to analyze the sources of clinical efficacy and safety, cost, and health utility parameters in model-based CUAs for advanced lung cancer in the United States (US) and China.

Methods: We systematically reviewed model-based CUAs of pharmacological treatments for advanced lung cancer published between January 1, 2018 and March 31, 2025 in the US and Chinese setting. We classified the source of each parameter and retrieved the references cited for the parameters to analyze the citation path and level until we identified the original studies. We also compared the disease and region of parameters used in CUAs with those reported in the original studies.

Results: A total of 235 studies involving 10,005 parameters were included. Nearly half of the parameters (49.9%) were derived from published literature. Meanwhile, 17.7% had unidentifiable sources and 1.3% were based on assumptions. Among parameters cited from published literatures, 90.7% were first-level citations, but only 64.2% of cost parameters met this standard. Additionally, 30.8% of parameters showed discrepancies in disease or region between the CUAs and original studies. Parameter source distributions were similar between Chinese and US models. However, substantial differences were observed between Chinese and US models in the citation levels of cost parameters and the use of non-local utility data.

Conclusions: This study highlights challenges in parameter citation and the use of data inconsistent with the target disease and region in model-based CUAs. Enhancing transparency requires direct citation of original studies and generation of disease- and region-specific data to support robust economic evaluations.

Keywords: Cost-utility analysis; Economic evaluation; Lung cancer; Model transparency; Sources of parameters.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Model-based cost-utility analysis inclusion flowchart and identification of input parameters
Fig. 2
Fig. 2
Citation paths and levels of PFS utility (A), PD utility (B), cost of best supportive care (C) and cost of end of life parameters (D) from literatures in cost-utility analysis in Chinese setting
Fig. 3
Fig. 3
Citation paths and levels of PFS utility (A), PD utility (B), cost of best supportive care (C) and cost of end of life parameters (D) from literatures in cost-utility analysis in the US setting
See this image and copyright information in PMC

References

    1. Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol. 2023;20(9):624–39. - PubMed
    1. Scott EC, Baines AC, Gong Y, Moore R Jr., Pamuk GE, Saber H, Subedee A, Thompson MD, Xiao W, Pazdur R, et al. Trends in the approval of cancer therapies by the FDA in the twenty-first century. Nat Rev Drug Discov. 2023;22(8):625–40. - PubMed
    1. Zhang Y, Naci H, Wagner AK, Xu Z, Yang Y, Zhu J, Ji J, Shi L, Guan X. Overall survival benefits of Cancer drugs approved in China from 2005 to 2020. JAMA Netw Open. 2022;5(8):e2225973–2225973. - PMC - PubMed
    1. Egilman AC, Rome BN, Kesselheim AS. Added therapeutic benefit of Top-Selling Brand-name drugs in medicare. JAMA. 2023;329(15):1283–9. - PMC - PubMed
    1. Jenei K, Raymakers AJN, Bayle A, Berger-Thurmel K, Cherla A, Honda K, Jackson C, Karikios D, Trapani D, Berry S, et al. Health technology assessment for cancer medicines across the G7 countries and oceania: an international, cross-sectional study. Lancet Oncol. 2023;24(6):624–35. - PubMed

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