Development of the Cystic Fibrosis Questionnaire-Revised-8 Dimensions: Estimating Utilities From the Cystic Fibrosis Questionnaire-Revised
Bibliographic record
Abstract
OBJECTIVES: Cystic fibrosis (CF) limits survival and negatively affects health-related quality of life (HRQOL). Cost-effectiveness analysis (CEA) may be used to make reimbursement decisions for new CF treatments; nevertheless, generic utility measures used in CEA, such as EQ-5D, are insensitive to meaningful changes in lung function and HRQOL in CF. Here we develop a new, CF disease-specific, preference-based utility measure based on the adolescent/adult version of the Cystic Fibrosis Questionnaire-Revised (CFQ-R), a widely used, CF-specific, patient-reported measure of HRQOL. METHODS: Blinded CFQ-R data from 4 clinical trials (NCT02347657, NCT02392234, NCT01807923, and NCT01807949) were used to identify discriminating items for a classification system using psychometric (eg, factor and Rasch) analyses. Thirty-two health states were selected for a time trade-off (TTO) exercise with a representative sample of the UK general population. TTO utilities were used to estimate a preference-based scoring algorithm by regression analysis (tobit models with robust standard errors clustered on participants with censoring at -1). RESULTS: A classification system with 8 dimensions (CFQ-R-8 dimensions; physical functioning, vitality, emotion, role functioning, breathing difficulty, cough, abdominal pain, and body image) was generated. TTO was completed by 400 participants (mean age, 47.3 years; 49.8% female). Among the regression models evaluated, the tobit heteroscedastic-ordered model was preferred, with a predicted utility range from 0.236 to 1, no logical inconsistencies, and a mean absolute error of 0.032. CONCLUSION: The CFQ-R-8 dimensions is the first disease-specific, preference-based scoring algorithm for CF, enabling estimation of disease-specific utilities for CEA based on the well-validated and widely used CFQ-R.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".