Using the health assessment questionnaire to estimate preference‐based single indices in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To estimate the relationship between preference-based measures, EuroQol (EQ-5D) and SF-6D, and the Health Assessment Questionnaire (HAQ) disability index (DI) in patients with rheumatoid arthritis (RA), and to characterize components that are predictors of health utility. METHODS: Patients with RA participating in 2 studies in the UK (n = 151) and Canada (n = 319) completed the HAQ, EQ-5D, and Short Form 36 (SF-36). The SF-36, a generic measure of quality of life, was converted into the preference-based SF-6D. From these results we developed models of the relationship between the HAQ and SF-6D and EQ-5D using various regression analyses. RESULTS: The optimal model developed for the EQ-5D entered levels for each item as independent variables (model 5). A root mean square error (RMSE) of 0.18 suggested relatively good predictive ability. For the SF-6D, RMSEs were lower (0.09), suggesting better predictions than for the EQ-5D, but models with more explanatory variables did not improve results (model 2 or 4 optimal). The models were able to predict actual SF-6D and EQ-5D across the range of the HAQ DI. CONCLUSION: Our approach enabled calculations of quality-adjusted life years from existing trials where only the HAQ was measured. All aspects of the HAQ may not be reflected in the preference-based measures, and this method is suboptimal to direct measurement of health state utility in clinical trials. Given this limitation, our approach provides an alternative for researchers who need health-state utility values, but had not included a preference-based measure in their clinical study because of resource constraints or a desire to limit patient burden.
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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 | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".