Exploring the validity of estimating EQ-5D and SF-6D utility values from the health assessment questionnaire in patients with inflammatory arthritis
Bibliographic record
Abstract
BACKGROUND: Utility scores are used to estimate Quality Adjusted Life Years (QALYs), applied in determining the cost-effectiveness of health care interventions. In studies where no preference based measures are collected, indirect methods have been developed to estimate utilities from clinical instruments. The aim of this study was to evaluate a published method of estimating the EuroQol-5D (EQ-5D) and Short Form-6D (SF-6D) (preference based) utility scores from the Health Assessment Questionnaire (HAQ) in patients with inflammatory arthritis. METHODS: Data were used from 3 cohorts of patients with: early inflammatory arthritis (<10 weeks duration); established (>5 years duration) stable rheumatoid arthritis (RA); and RA being treated with anti-TNF therapy. Patients completed the EQ-5D, SF-6D and HAQ at baseline and a follow-up assessment. EQ-5D and SF-6D scores were predicted from the HAQ using a published method. Differences between predicted and observed EQ-5D and SF-6D scores were assessed using the paired t-test and linear regression. RESULTS: Predicted utility scores were generally higher than observed scores (range of differences: EQ-5D 0.01 - 0.06; SF-6D 0.05 - 0.10). Change between predicted values of the EQ-5D and SF-6D corresponded well with observed change in patients with established RA. Change in predicted SF-6D scores was, however, less than half of that in observed values (p < 0.001) in patients with more active disease. Predicted EQ-5D scores underestimated change in cohorts of patients with more active disease. CONCLUSION: Predicted utility scores overestimated baseline values but underestimated change. Predicting utility values from the HAQ will therefore likely underestimate the QALYs of interventions, particularly for patients with active disease. We recommend the inclusion of at least one preference based measure in future clinical 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 | 0.042 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".