Estimating an EQ-5D-5L Value Set for China
Bibliographic record
Abstract
OBJECTIVES: To estimate a five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) value set for China using the health preferences of residents living in the urban areas of the country. METHODS: The values of a subset of the EQ-5D-5L-defined health states (n = 86) were elicited using the time trade-off (TTO) technique from a sample of urban residents (n = 1271) recruited from five Chinese cities. In computer-assisted personal interviews, participants each completed 10 TTO tasks. Two additive and two multiplicative regression models were evaluated for their performance in describing the relationship between TTO values and health state characteristics using a cross-validation approach. Final values were generated using the best-performed model and a rescaling method. RESULTS: The 8- and 9-parameter multiplicative models unanimously outperformed the 20-parameter additive model using a random or fixed intercept in predicting values for out-of-sample health states in the cross-validation analysis and their coefficients were estimated with lower standard errors. The prediction accuracies of the two multiplicative models measured by the mean absolute error and the intraclass correlation coefficient were very similar, thus favoring the more parsimonious model. CONCLUSIONS: The 8-parameter multiplicative model performed the best in the study and therefore was used to generate the EQ-5D-5L value set for China. We recommend using rescaled values whereby 1 represents the value of instrument-defined full health in economic evaluation of health technologies in China whenever the EQ-5D-5L data are available.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".