Canadian Valuation of EQ-5D Health States: Preliminary Value Set and Considerations for Future Valuation Studies
Bibliographic record
Abstract
BACKGROUND: The EQ-5D is a preference based instrument which provides a description of a respondent's health status, and an empirically derived value for that health state often from a representative sample of the general population. It is commonly used to derive Quality Adjusted Life Year calculations (QALY) in economic evaluations. However, values for health states have been found to differ between countries. The objective of this study was to develop a set of values for the EQ-5D health states for use in Canada. METHODS: Values for 48 different EQ-5D health states were elicited using the Time Trade Off (TTO) via a web survey in English. A random effect model was fitted to the data to estimate values for all 243 health states of the EQ-5D. Various model specifications were explored. Comparisons with EQ-5D values from the UK and US were made. Sensitivity analysis explored different transformations of values worse than dead, and exclusion criteria of subjects. RESULTS: The final model was estimated from the values of 1145 subjects with socio-demographics broadly representative of Canadian general population with the exception of Quebec. This yielded a good fit with observed TTO values, with an overall R2 of 0.403 and a mean absolute error of 0.044. CONCLUSION: A preference-weight algorithm for Canadian studies that include the EQ-5D is developed. The primary limitations regarded the representativeness of the final sample, given the language used (English only), the method of recruitment, and the difficulty in the task. Insights into potential issues for conducting valuation studies in countries as large and diverse as Canada are gained.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".