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 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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".