A Comparison of International Modeling Methods for Evaluating Health Economics of Colorectal Cancer Screening: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Cost-effectiveness analysis (CEA) is an accepted approach to evaluate cancer screening programs. CEA estimates partially depend on modeling methods and assumptions used. Understanding common practice when modeling cancer relies on complete, accessible descriptions of prior work. This review's objective is to comprehensively examine published CEA modeling methods used to evaluate colorectal cancer (CRC) screening from an aspiring modeler's perspective. It compares existing models, highlighting the importance of precise modeling method descriptions and essential factors when modeling CRC progression. METHODS: MEDLINE, EMBASE, Web of Science, and Scopus electronic databases were used. The Consolidated Health Economic Evaluation Reporting Standards statement and data items from previous systematic reviews formed a template to extract relevant data. Specific focus included model type, natural history, appropriate data sources, and survival analysis. RESULTS: Seventy-eight studies, with 52 unique models were found. Twelve previously published models were reported in 39 studies, with 39 newly developed models. CRC progression from the onset was commonly modeled, with only 6 models not including it as a model component. CONCLUSIONS: Modeling methods needed to simulate CRC progression depend on the natural history structure and research requirements. For aspiring modelers, accompanying models with clear overviews and extensive modeling assumption descriptions are beneficial. Open-source modeling would also allow model replicability and result in appropriate decisions suggested for CRC screening programs.
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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 | 0.077 | 0.193 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".