How was published evidence used in model-based cost-utility analysis for lung cancer?
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.218 | 0.701 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.043 | 0.045 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".