Stratified cost‐effectiveness analysis: a framework for establishing efficient limited use criteria
Bibliographic record
Abstract
The cost-effectiveness of new health care technologies is conditional upon who receives what therapy and under what circumstances. Understanding this heterogeneity in cost-effectiveness, health care payers often limit reimbursement of therapies to a more restrictive sub-group of patients than that indicated in a product's licensing. Such limits may be based upon clinical or demographic criteria that are prognostic of costs, outcomes or both. However, there is little guidance on how to estimate and interpret stratified cost-effectiveness analysis. In this paper we present a framework for estimating the benefits from stratification that permits consideration of both the opportunity cost resulting from a lack of adherence with criteria and the efficiency loss associated with incorporating equity concerns.
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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 | 0.188 | 0.326 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".