Bibliographic record
Abstract
BACKGROUND: The EuroQol Group is evaluating the use of discrete choice experiments (DCEs) in valuing health states from the 5-level EQ-5D. Notably, a discrete choice (DC) model yields a latent utility that is ordinal and unbounded, whereas health utilities must have interval properties and be anchored at 0 (representing death) and 1 (representing full health). Latent utilities must therefore be transformed to health utilities. This pilot study investigated the feasibility of performing such a transformation. METHODS: 545 respondents from Canada and 403 respondents from the UK each completed a series of DC and time tradeoff (TTO) tasks. Generalized linear mixed models were used to derive latent utilities. Linear regression models incorporating logarithmic and polynomial terms, as well as nonparametric LOESS and spline models, were assessed as candidate functions for transforming the latent utilities onto the health utilities. RESULTS: There was a high correlation between health utilities measured through TTO tasks and latent utilities derived from modeling of DC data (Spearman rho of 0.79 in Canada and 0.86 in the UK). All transforming functions explained the between-state variation in health utilities and, upon cross-validation, had minimal bias and small mean squared errors. Although the transformation functions derived through linear regression had the desirable feature of being monotone, the LOESS transform in Canada and the spline transform in the UK lacked monotonicity. CONCLUSIONS: This pilot study suggests that transforming latent utilities to health utilities is feasible, and the study provides preliminary evidence that linear regression involving polynomial and logarithmic terms may be more desirable than nonparametric spline or LOESS functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.020 |
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; both teacher heads agree on what is shown here.
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".