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Updated: Sep 19, 2025

Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
Published on: November 11, 2020
How was published evidence used in model-based cost - utility analysis for lung cancer?
Haijing Guan1,2, Chunping Wang3,4, Ruowei Xiao3,4
1Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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.
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