Comparison of methods for anchoring latent values on the full health-dead utility scale
Bibliographic record
Abstract
A key challenge in ordinal methods is to anchor estimated health utility values onto the full health-dead scale. This study assessed five methods of anchoring. Data were collected between 2016 and 2020 through two surveys conducted in the Quebec general population, with 1,176 and 908 respondents. Health utilities for the Short-Form 6-Dimension version 2 (SF-6Dv2) were estimated using ranking, composite time trade-off (cTTO), discrete choice experiment without (DCE) and with duration (DCE TTO ) methods. Anchoring was performed using five approaches: the dead state for ranking (Rank), linear mapping for DCE (DCE Mapping ), mean value for the worst health state (DCE WHS ), hybrid modeling (DCE Hybrid ), and duration (DCE TTO ). Conditional logit was used for rank and DCE approaches, while a hybrid model and generalized least squares (GLS) were applied for the DCE Hybrid and cTTO methods, respectively. Approaches were compared based on the sign and ordering of their coefficients, the mean absolute difference (MAD) between the observed mean cTTO values and the estimates of models, and the overall pattern of their estimations. A total of 17,200, 59,960, 8,500, and 16,464 observations were included for the DCE TTO , ranking, cTTO, and DCE methods, respectively. The DCE Hybrid method achieved the lowest MAD (0.056) from observed cTTO values, while DCE TTO method had the highest (MAD = 0.423). Ranking and DCE Mapping tended to underestimate utilities for better health states and overestimate for severe ones, while DCE WHS and DCE TTO underestimated health utilities for all health states. Visual comparisons confirmed that predictions from models closely aligned with observed cTTO values for better health states. Hybrid anchoring method demonstrated better performance in predicting observed cTTO values, whereas DCE TTO model showed greater deviations and a tendency to underestimate health state values. Hybrid anchoring method also appeared as the most appropriate and reliable approach for anchoring ordinal utility data to the full health–dead scale. • Anchoring is crucial for translating DCE-derived preferences onto the full health–dead scale. • This is the first study comparing all major anchoring approaches to generate utilities. • The duration-based anchoring method (DCE TTO ) produced greater deviations and underestimated health state values. • The hybrid anchoring approach produced the best results.
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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 | 0.088 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".