The Impact of Alternative Specifications of Uncertainty Relating to Extrapolation in Decision Models
Bibliographic record
Abstract
Economic evaluations that incorporate value-of-information analysis frequently conclude that the greatest information value relates to replicating short-term clinical trials. This study builds on recent guidance relating to extrapolation in economic evaluation by assessing the impact of alternative approaches to representing the uncertainty around unobserved/extrapolated data with respect to incremental outcomes and value of information. When the uncertainty over unobserved and observed data is considered distinct but correlated (i.e., has a joint distribution), it is demonstrated that the value to replicating short-term clinical studies is lessened and that further studies relating to the unobserved periods likely provide more value. Highlights: Current practice in economic evaluation often involves the inappropriate specification of uncertainty with respect to unobserved data.Appropriate specification of uncertainty will lead to more pertinent recommendations over future clinical studies.
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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 | 0.212 | 0.537 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".