Valuing health-related quality of life using a hybrid approach: Tunisian value set for the EQ-5D-3L
Bibliographic record
Abstract
OBJECTIVE: To develop a value set for EQ-5D-3L based on the societal preferences of the Tunisian population. METHODS: A representative sample of the Tunisian general population was obtained through multistage quota sampling involving age, gender and region. Participants (n = 327), aged above 20 years, were interviewed using the EuroQol Portable Valuation Technology in face-to-face computer-assisted interviews. Participants completed 10 composite time trade-off (cTTO) and 10 discrete choice experiments (DCE) tasks. Utility values for the EQ-5D-3L health states were estimated using regression modeling. The cTTO and DCE data were analyzed using linear and conditional logistic regression modeling, respectively. Multiple hybrid models were computed to analyze the combined data and were compared on goodness of fit measured by the Akaike information criterion (AIC). RESULTS: A total of 300 participants with complete data that met quality criteria were included. All regression models showed both logical consistency and significance with respect to the parameter estimates. A hybrid model accounting for heteroscedasticity presented the lowest value for the AIC among the hybrid models. Hence, it was used to construct the Tunisian EQ-5D-3L valuation set with a range of predicted values from - 0.796 to 1.0. CONCLUSION: This study provides utility values for EQ-5D-3L health states for the Tunisian population. This value set will be used in economic evaluations of health technologies and for Tunisian health policy decision-making.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".