An Exploration on Attribute Non-attendance Using Discrete Choice Experiment Data from the Irish EQ-5D-5L National Valuation Study
Bibliographic record
Abstract
BACKGROUND: Generic measures of health-related quality of life (HRQoL) permit comparisons of competing demands for healthcare resources using outcomes that reflect the preferences of tax payers. EQ-5D instruments are the most commonly used generic, preference-based measures of HRQoL. The EQ-5D-5L enables respondents to describe their health state using five dimensions of health, each with five response levels. The standardised protocol for the valuation of EQ-5D-5L health states comprises use of the composite time trade-off valuation technique, supplemented by a discrete choice experiment (DCE). OBJECTIVE: This paper presents the first exploration on attribute non-attendance (ANA) to the dimensions of the EQ-5D-5L using DCE data collected following the standardised protocol. METHOD: This paper uses the equality constrained latent class model and the endogenous attribute attendance model to examine ANA to the dimensions of the EQ-5D-5L. RESULTS: The results suggest that respondents are less likely to consider the physical dimensions of the EQ-5D-5L (such as self-care and usual activities) when evaluating the health states. The effects of ANA on utility scores depends on the interpretation of the underlying reasons for ANA. CONCLUSIONS: We recommend that future value sets based in whole or in part on DCE data examine the impact of and reasons for non-attendance in national valuation studies.
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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 | 0.130 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".