Cancer-Specific Health Utilities: Evaluation of Core Measurement Properties of the EORTC QLU-C10D in Lung Cancer Patients—Data from Four Multicentre LUX-Lung Trials, Applying Six Country Tariffs
Bibliographic record
Abstract
BACKGROUND: Cost-utility analysis generally requires valid preference-based measures (PBMs) to assess the utility of patient health. While generic PBMs are widely used, disease-specific PBMs may capture additional aspects of health relevant for certain patient populations. This study investigates the construct and concurrent criterion validity of the cancer-specific European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility-Core 10 dimensions (QLU-C10D) in non-small-cell lung cancer patients. METHODS: We retrospectively analysed data from four multicentre LUX-Lung trials, all of which had administered the EORTC Quality of Life Questionnaire (QLQ-C30) and the EQ-5D-3L. We applied six country-specific value sets (Australia, Canada, Italy, the Netherlands, Poland, and the United Kingdom) to both instruments. Criterion validity was assessed via correlations between the instruments' utility scores. Correlations of divergent and convergent domains and Bland-Altman plots investigated construct validity. Floor and ceiling effects were assessed. RESULTS: The comparison of the EORTC QLU-C10D and EQ-5D-3L produced homogenous results for five of the six country tariffs. High correlations of utilities (r > 0.7) were found for all country tariffs except for the Netherlands. Moderate to high correlations of converging domain pairs (r from 0.472 to 0.718) were found with few exceptions, such as the Social Functioning-Usual Activities domain pair (max. r = 0.376). For all but the Dutch tariff, the EORTC QLU-C10D produced consistently lower utility values compared to the EQ-5D-3L (x̄ difference from - 0.082 to 0.033). Floor and ceiling effects were consistently lower for the EORTC QLU-C10D (max. 4.67% for utilities). CONCLUSIONS: The six country tariffs showed good psychometric properties for the EORTC QLU-C10D in lung cancer patients. Criterion and construct validity was established. The QLU-C10D showed superior measurement precision towards the upper and lower end of the scale compared to the EQ-5D-3L, which is important when cost-utility analysis seeks to measure health change across the severity spectrum.
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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 | 0.046 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".