International standards for health economic evaluation with a focus on the German approach
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Health economic evaluation (HEE) is increasingly used in healthcare decision-making on the allocation of limited resources in national healthcare systems. Although the methods used for HEE vary in different countries, all economic evaluations address two questions: Are limited resources used optimally? Is value for money achieved in their use? Our objective is to explain some fundamental concepts in HEE and how these concepts are adapted in different countries, notably in Germany. METHODS: We performed a bibliographic search to identify existing methods of health economic evaluation of new drugs used by the official agencies of 11 countries (Austria, Australia, Canada, Finland, France, the Netherlands, Norway, New Zealand, Sweden, the United States and England and Wales) and compared them with that used by the German national agency IQWiG. RESULTS AND DISCUSSION: All countries considered follow internationally established standards of HEE. The majority of countries, including Germany, utilize primary outcome parameters such as disease-related morbidity and mortality for assessing relative efficacy and effectiveness. The most frequently recommended form of health economic evaluation is the cost-utility analysis (CUA). The German IQWIG is the only HTA body to use the cost-benefit concept of 'efficiency frontier' in its assessment. WHAT IS NEW AND CONCLUSION: While the core principles of HEE are the same worldwide, there is a lack of harmonization in the details. This requires resource-consuming adaptations in the analyses to meet different national requirements. We describe the core principles of HEE as a common basis for further discussions by all stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".