A Model-Based Estimate of the Cost-Effectiveness Threshold in Germany
Bibliographic record
Abstract
PURPOSE: Value-based pricing of new, innovative health technologies defined as pricing through economic evaluation requires the use of a basic cost-effectiveness threshold. This study presents a cost-effectiveness model that determines the cost-effectiveness threshold for life-extending new, innovative technologies based on health system opportunity costs. METHODS: To estimate health system opportunity costs, the study used German data and examined the period between 1896 and 2014. To this end, it determined intertemporal differences in the remaining lifetime spending and life expectancy by age and gender. To account for the age composition of the population, it weighted age-specific intertemporal changes in the remaining lifetime spending and life expectancy by the age-specific population size. To estimate life expectancy gains solely attributable to the health care system, it used aggregated data on amenable mortality. It calculated the cost-effectiveness ratio of health care spending in the German health care system on average and at the margin. RESULTS: Based on the cost-effectiveness ratio of health care spending at the margin, the threshold value for life-prolonging new, innovative technologies was at least €42,634 per life-year gained, with a point estimate of €88,107 per life-year gained. Based on the average ratio, the threshold value dropped below €34,000 per life-year gained. CONCLUSION: This study provides new evidence on the cost-effectiveness threshold for value-based pricing of new, innovative technologies. Data from Germany suggest that a threshold value based on health care spending at the margin is considerably higher than that based on the average ratio.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".