Principles of good modeling practice in healthcare cost-effectiveness studies
Bibliographic record
Abstract
Decision analytic models are increasingly being used to present information on the costs and effects of both new and existing healthcare technologies. However, despite this increase, the literature on what constitutes 'quality' or 'good practice' in modeling is sparse, confusing and often conflicting. As a result there is a need to summarize these quality assurance and good practice principles into a framework that is useful for modelers and users of models alike. This review has attempted to summarize these principles into five broad categories that will assist in assessing whether a model should be considered 'SAVED' (has structural integrity, uses appropriate input data and calculation methods, validates the model output, has extensive use and reporting of sensitivity analysis, and if there is detailed and unbiased reporting and interpretation of study findings). These principles span every aspect of the cost-effectiveness analysis from model conception, development and calculation, to presentation and interpretation of the results. Modelers are strongly encouraged to actively consider these principles throughout the entire process of model development, analysis and write up. Users of modeling studies should be familiar with these principles in order to correctly appraise studies for their applicability, validity and interpretability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.339 | 0.403 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".