Transforming Latent Utilities to Health Utilities: East Does Not Meet West
Bibliographic record
Abstract
Discrete choice experiments (DCEs) are a promising alternative to more resource-intensive preference elicitation methods such as time trade-off (TTO), as pairwise comparisons are more amenable to online completion, which can save time and money. However, modeling DCE data produces latent utilities which are on an unknown scale. Therefore, latent utilities need to be transformed to a full health-dead scale before they can be used in quality-adjusted life year calculations. We aimed to explore transformation functions from DCE-derived latent utilities to TTO-derived health utilities. We used EQ-5D-5L valuation data from eight different countries that collected both DCE and TTO data by using a standardized protocol. Results found less variation in the function that transformed latent utilities to health utilities in the western countries than in the eastern countries. While a global transformation function is not recommended, results suggest that regional transformation functions could potentially be used to derive health utilities from DCE data. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.063 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".