Content and Construct Validity, Reliability, and Responsiveness of the Rheumatoid Arthritis Flare Questionnaire: OMERACT 2016 Workshop Report
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Rheumatoid Arthritis (RA) Flare Group was established to develop a reliable way to identify and measure RA flares in randomized controlled trials (RCT). Here, we summarized the development and field testing of the RA Flare Questionnaire (RA-FQ), and the voting results at OMERACT 2016. METHODS: Classic and modern psychometric methods were used to assess reliability, validity, sensitivity, factor structure, scoring, and thresholds. Interviews with patients and clinicians also assessed content validity, utility, and meaningfulness of RA-FQ scores. RESULTS: People with RA in observational trials in Canada (n = 896) and France (n = 138), and an RCT in the Netherlands (n = 178) completed 5 items (11-point numerical rating scale) representing RA Flare core domains. There was moderate to high evidence of reliability, content and construct validity, and responsiveness. Factor analysis supported unidimensionality. Rasch analysis showed acceptable fit to the Rasch model, with items and people covering a broad measurement continuum and evidence of appropriate targeting of items to people, ordered thresholds, minimal differential item functioning by language, sex, or age. A summative score across items is defensible, yielding an interval score (0-50) where higher scores reflect worsening flare. The RA-FQ received endorsement from 88% of attendees that it passed the OMERACT Filter 2.0 "Eyeball Test" for instrument selection. CONCLUSION: The RA-FQ has been developed to identify and measure RA flares. Its review through OMERACT Filter 2.0 shows evidence of reliability, content and construct validity, and responsiveness. These properties merit its further validation as an outcome for clinical trials.
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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 | 0.109 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".