Cost-effectiveness analysis for multinational clinical trials
Bibliographic record
Abstract
Clinical trials of cost-effectiveness are often conducted in more than one country. The two most common ways of dealing with the multinational nature of the data are either to calculate a pooled estimate or to stratify results by country. Since the between-country heterogeneity in costs is potentially substantial, pooled estimates may be difficult to interpret for any one country. Policy decisions are often made at a national level, and so country-specific results are important. However, country-specific analyses will be based on fewer patients and will often fail to provide adequate precision for statistical analyses. Shrinkage estimation is a compromise between these two methods and has been used successfully in other fields. These estimates are country-specific yet less variable than those derived through a subgroup approach. Univariate and multivariate shrinkage estimators for costs and effects are proposed, then compared with one another and to the traditional methods in a simulation study. The methods are illustrated using data from a multinational trial evaluating the cost-effectiveness of three thrombolytic drug regimens in patients with acute myocardial infarction.
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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 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".